CHAPTER its own fair share of security threats

CHAPTER 1
INTRODUCTION
1.1 INFORMATION CONSISTENCY
Numerous developments are being introduced as the epoch of Cloud Computing, which is an cyberspace-based progress and use of supercomputer expertise. Most powerful processors which were too expensive to begin with have become cheaper by the help of pooling processing power and providing the processing power on demand. The development of high speed internet with increased bandwidth have increased the quality of services leading to better customer satisfaction which the most primitive goal of any organization.
The migration of data from the users’ computer to the remote data centers have the provided the customer with great and reliable convenience. Amazon simple storage services are the well-known examples which are one of the pioneers of cloud services. The eliminate the need of maintain the data on a local system which is a huge boost for increasing quality of service. But due to this the customers are always as the mercifulness of the cloud service provider as their downtime causes the user to be unable to access his own data. Since every coin has two sides, likewise cloud computing has its own fair share of security threats and also there may be some threats which are yet to be discovered. Considering from the user’s point of view, he wants his data to be secure therefore, data security is the most important aspect which will ultimately lead to the customer satisfaction. The users’ have limited control on their own data so the conventional cryptography measures cannot be adopted. Thus, the data stored on the cloud should be verified occasionally to ensure the data has not been modified without informing the owner. The data which is rarely used is sometimes moved to lower tier storage making it more vulnerable for attacks. On the other note, Cloud Computing not only stores the data but also provides the user with functionality like modifying the data, appending some information to it or permanently deleting the data. To assure the integrity of data various hashing algorithms can be used to create checksums which will alert the user about the data modifications.

1.2 PROBLEM DEFINITION
Firstly, traditional cryptographic primitives for the purpose of data security protection cannot be directly adopted due to the users’ loss control of data under Cloud Computing. Whenever it comes to the matter relating to cloud services the user is put at a disadvantage regarding to the security of the file. Basically the file is stored on a server which is a pool resource that is any one with user’s credentials can access the file and if in case the attacker comes to know about the password as well as the encryption keys the attacker can modify the file contents, thus making the information stored in the file to be accessed by the unauthorized user. So, the problem is that what if someone copy’s your work and claims to be his own work. Anything we design , anything we invent is governed by the principle of whether or not it guarantees customer satisfaction.
Hence, the problem is underlying whether the customer can rest assured that his data is safe from unauthorized access or not.

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1.3 PROJECT PURPOSE
In our purposed system, we provide assurance to the user that his information is safe by “implementing a system which provides security mechanisms by offering three levels of security”. Concerning about the data security part, our system is divided mainly into three modules named “IP triggering” module, “client-authentication” module and “redirecting” module. The system generates a user password and a key which is used for client authentication.
The algorithm generates two keywords 8 bit length consisting of combinations of characters, special characters, and numbers which is used for client authorization and file authorization.
Questions may arise as why do we use keys of 8 bit length only? The purpose of our system is to prevent illegal data access if the users’ credential are compromised. By testing against weak algorithms which are easier to crack we design our system to be more robust.

1.4 PROJECT FEATURES
Our scheme would be to prevent illegal access of users’ data. A user after getting himself registered on the system will have the advantage of different layers of security. The most primitive work our system is to inform the user that his data has been accessed from an unregistered ip by using mail triggering events. For login, the attacker tries to access the file by using the credentials stolen from the victim, and upon entering is provided with a dialog box to enter a key. The attacker tries to enter the key which won’t be accepted by any means. The attacker is provided with a three tries so that he can go back. After 3 tries, the attacker is provided with the access of the fake file which is implemented by the redirection module.

1.5 MODULES DESCRIPTION
1.5.1 CLOUD STORAGE
Data outsourcing to cloud storage servers is raising trend among many firms and users owing to its economic advantages. This essentially means that the owner (client) of the data moves its data to a third party cloud storage server which is supposed to – presumably for a fee – faithfully store the data with it and provide it back to the owner whenever required. Cloud storage increases maintainability and decreases storage cost associated with storage.

1.5.2 SIMPLY ARCHIVES
This problem tries to obtain and verify a proof that the data that is stored by a user at remote data storage in the cloud (called cloud storage archives or simply archives) is not modified by the archive and thereby the integrity of the data is assured.
The file is encrypted using symmetric key algorithms ( same key is used for encryption and decryption of data) before storing it in cloud storage. Cloud archive is not cheating the owner, if cheating, in this context, means that the storage archive might delete some of the data or may modify some of the data.
While developing proofs for data possession at untrusted cloud storage servers we are often limited by the resources at the cloud server as well as at the client.

1.5.3 SENTINELS
In this scheme, unlike in the key-hash approach scheme, only a single key can be used irrespective of the size of the file or the number of files whose retrievability it wants to verify. Also the archive needs to access only a small portion of the file F unlike in the key-has scheme which required the archive to process the entire file F for each protocol verification. If the prover has modified or deleted a substantial portion of F, then with high probability it will also have suppressed a number of sentinels.

1.5.4 VERIFICATION PHASE:
The verifier before storing the file at the archive preprocesses the file and appends some Meta data to the file and stores at the archive. At the time of verification the verifier uses this Meta data to verify the integrity of the data. If the metadata matches the already stored metadata in database then there is inconsistency in file and user user is alerted with a warning message.l It is important to note that our proof of information consistency protocol just checks the integrity of data i.e. if the data has been illegally modified or deleted. It does not prevent the archive from modifying the data.

CHAPTER 2
LITERATURE SURVEY
2.1 CLOUD COMPUTING
Literature survey is the most important step in software development process. Before developing the tool it is necessary to determine the time factor, economy and company strength. Once these things are satisfied, then next steps is to determine which operating system and language can be used for developing the tool. Once the programmers start building the tool the programmers need lot of external support. This support can be obtained from senior programmers, from book or from websites. Before building the system the above consideration are taken into account for developing the proposed system. We have to analysis the Cloud Computing Outline Survey:
Cloud Computing
Cloud computing providing unlimited infrastructure to store and execute customer data and program. As customers you do not need to own the infrastructure, they are merely accessing or renting; they can forego capital expenditure and consume resources as a service, paying instead for what they use.
Instead of running programs and data on an individual desktop computer, everything is hosted in the “cloud”—a nebulous assemblage of computers and servers accessed via the Internet. Cloud computing lets you access all your applications and documents from anywhere in the world, freeing you from the confines of the desktop and making it easier for group members in different locations to collaborate.
In short, cloud computing enables a shift from the computer to the user, from applications to tasks, and from isolated data to data that can be accessed from anywhere and shared with anyone. The user no longer has to take on the task of data management; he doesn’t even have to remember where the data is. All that matters is that the data is in the cloud, and thus immediately available to that user and to other authorized users.

Benefits of Cloud Computing:
• Minimized Capital expenditure
• Location and Device independence
• Utilization and efficiency improvement
• Very high Scalability
• High Computing power
How secure is encryption Scheme:
• Is it possible for all of my data to be fully encrypted?
• What algorithms are used?
• Who holds, maintains and issues the keys?
• Encryption accidents can make data totally unusable.
• Encryption can complicate availability Solution

2.2 EXISTING SYSTEM
As data generation is far outpacing data storage it proves costly for small firms to frequently update their hardware whenever additional data is created. Also maintaining the storages can be a difficult task. It transmitting the file across the network to the client can consume heavy bandwidths. The problem is further complicated by the fact that the owner of the data may be a small device, like a PDA (personal digital assist) or a mobile phone, which have limited CPU power, battery power and communication bandwidth.
Disadvantages:
• The main drawback of this scheme is the high resource costs it requires for the implementation.
• Also computing hash value for even a moderately large data files can be computationally burdensome for some clients (PDAs, mobile phones, etc).
• Data encryption is large so the disadvantage is small users with limited computational power (PDAs, mobile phones etc.).
• Consumption of large amount of bandwidth in transmission of file.

2.3 PROPOSED SYSTEM
One of the important concerns that need to be addressed is to assure the customer of the integrity i.e. correctness of his data in the cloud. As the data is physically not accessible to the user the cloud should provide a way for the user to check if the integrity of his data is maintained or is compromised. In this paper we provide a scheme which gives a proof of data integrity in the cloud which the customer can employ to check the correctness of his data in the cloud. This proof can be agreed upon by both the cloud and the customer and can be incorporated in the Service level agreement (SLA). It is important to note that our proof of data integrity protocol just checks the integrity of data i.e. if the data has been illegally modified or deleted.
Advantages:
? Apart from reduction in storage costs data outsourcing to the cloud also helps in reducing the maintenance.
? Avoiding local storage of data.
? By reducing the costs of storage, maintenance and personnel.
? It reduces the chance of losing data by hardware failures.
? Not cheating the owner.

2.4 SOFTWARE DESCRIPTION
2.4.1 C#
C# (pronounced see sharp) is a multi-paradigm programming language encompassing strong typing, imperative, declarative, functional, generic, object-oriented (class-based), and component-oriented programming disciplines. It was developed by Microsoft within its .NET initiative and later approved as a standard by Ecma (ECMA-334) and ISO (ISO/IEC 23270:2006). C# is one of the programming languages designed for the Common Language Infrastructure. Support for internationalization is very important.

The ECMA standard lists the design goals for C# as:
• C# language is intended to be a simple, modern, general-purpose, object-oriented programming language.
• The language, and implementations thereof, should provide support for software engineering principles such as strong type checking, array bounds checking, detection of attempts to use uninitialized variables, and automatic garbage collection. Software robustness, durability, and programmer productivity are important.
• The language is intended for use in developing software components suitable for deployment in distributed environments.
• Source code portability is very important, as is programmer portability, especially for those programmers already familiar with C and C++.
• C# is intended to be suitable for writing applications for both hosted and embedded systems, ranging from the very large that use sophisticated operating systems, down to the very small having dedicated functions.
• Although C# applications are intended to be economical with regard to memory and processing power requirements, the language was not intended to compete directly on performance and size with C or assembly language.

2.4.2 .NET FRAMWORK PLATFORM ARCHITECTURE
Microsoft .NET is a set of Microsoft software technologies for rapidly building and integrating XML Web services, Microsoft Windows-based applications, and Web solutions. The .NET Framework is a language-neutral platform for writing programs that can easily and securely interoperate. The .NET framework provides the foundation for components to interact seamlessly, whether locally or remotely on different platforms. It standardizes common data types and communications protocols so that components created in different languages can easily interoperate.

ASP.NET
XML WEB SERVICES Windows Forms
Base Class Libraries
Common Language Runtime
Operating System

Fig 2.1 NET Framework Architecture
The .NET Framework has two main parts:
1. The Common Language Runtime (CLR).
2. A hierarchical set of class libraries.
The CLR is described as the “execution engine” of .NET. It provides the environment within which programs run. The most important features are:
• Conversion from a low-level assembler-style language, called Intermediate Language (IL), into code native to the platform being executed on.
• Memory management, notably including garbage collection.
• Checking and enforcing security restrictions on the running code.
• Loading and executing programs, with version control and other such features.

Common Type System
The CLR uses something called the Common Type System (CTS) to strictly enforce type-safety. This ensures that all classes are compatible with each other, by describing types in a common way. CTS define how types work within the runtime, which enables types in one language to interoperate with types in another language, including cross-language exception handling.
As well as ensuring that types are only used in appropriate ways, the runtime also ensures that code doesn’t attempt to access memory that hasn’t been allocated to it.

Common Language Specification
The CLR provides built-in support for language interoperability. To ensure that you can develop managed code that can be fully used by developers using any programming language, a set of language features and rules for using them called the Common Language Specification (CLS) has been defined. Components that follow these rules and expose only CLS features are considered CLS-compliant.

THE CLASS LIBRARY
.NET provides a single-rooted hierarchy of classes, containing over 7000 types. The root of the namespace is called System; this contains basic types like Byte, Double, Boolean, and String, as well as Object. All objects derive from System. Object.
As well as objects, there are value types. Value types can be allocated on the stack, which can provide useful flexibility. There are also efficient means of converting value types to object types if and when necessary.

2.4.3 SQL-SERVER
The OLAP Services feature available in SQL Server version 7.0 is now called SQL Server 2000 Analysis Services. The term OLAP Services has been replaced with the term Analysis Services. Analysis Services also includes a new data mining component. The Repository component available in SQL Server version 7.0 is now called Microsoft SQL Server 2000 Meta Data Services. References to the component now use the term Meta Data Services. The term repository is used only in reference to the repository engine within Meta Data Services SQL-SERVER database consist of following type of objects:
1. TABLE
2. QUERY
3. FORM
4. REPORT
5. MACRO
TABLE:
A database is a collection of data about a specific topic.

VIEWS OF TABLE:
We can work with a table in two types,
1. Design View
2. Datasheet View
Design View
To build or modify the structure of a table we work in the table design view. We can specify what kind of data will be hold.
Datasheet View
To add, edit or analyses the data itself we work in tables datasheet view mode.
QUERY:
A query is a question that has to be asked the data. Access gathers data that answers the question from one or more table. The data that make up the answer is either dynaset (if you edit it) or a snapshot (it cannot be edited).
2.4.4 Jscript
JScript is Microsoft ‘s extended implementation of ECMAScript (ECMA262), an international standard based on Netscape’s JavaScript and Microsoft’s JScript languages. JScript is implemented as a Windows Script engine. This means that it can be “plugged in” to any application that supports Windows Script, such as Internet Explorer, Active Server Pages, and Windows Script Host. It also means that any application supporting Windows Script can use multiple languages – JScript, VBScript, Perl, and others.
JScript (and the other languages) can be used for both simple tasks (such as mouseovers on Web pages) and for more complex tasks (such as updating a database with ASP or running logon scripts for Windows NT ).
Windows Script relies on external “object models” to carry out much of its work. For example, Internet Explorer’s DOM provides objects such as ‘document’ and methods such as ‘write()’ to enable the scripting of Web pages.
JScript supports conditional compilation, which allows a programmer to selectively execute code within block comments. This is an extension to the ECMAScript standard that is not supported in other JavaScript implementations.
2.4.5 ASP or ACTIVE SERVER PAGES
Active Server Pages (ASP), also known as Classic ASP, was introduced in 1998 as Microsoft’s first server side scripting engine. ASP is a technology that enables scripts in web pages to be executed by an Internet server. ASP pages have the file extension .asp, and are normally written in VBScript. ASP.NET is a development framework for building web pages and web sites with HTML, CSS, JavaScript and server scripting.
When a browser requests an ASP file, the ASP.NET engine reads the file, compiles and executes the scripts in the file, and returns the result to the browser as plain HTML. ASP.NET supports three different development models:
Web Pages, MVC (Model View Controller), and Web Forms:
Web Pages
Single Pages Model MVC
Model View Controller Web Forms
Event Driven Model

1. Simplest ASP.NET model.
2. Similar to PHP and classic ASP.
3. Built-in templates and helpers for database, video, graphics, social media and more.
1. MVC separates web applications into 3 different components.
2. Models for data
Views for display
Controllers for input
1.Traditional ASP.NET event driven development model.
2. Web pages with added server controls, server events, and server code.
Fig 2.2 Development Models for ASP.NET

ASP.NET
ASP.NET is a new ASP generation. It is not compatible with Classic ASP, but ASP.NET may include Classic ASP. ASP.NET pages are compiled, which makes them faster than Classic ASP. ASP.NET has better language support, a large set of user controls, XML-based components, and integrated user authentication.
ASP.NET pages have the extension .aspx, and are normally written in VB (Visual Basic) or C# (C sharp). User controls in ASP.NET can be written in different languages, including C++ and Java.
Here are highlights of some of the new features:
Navigation: ASP.NET has a new higher-level model for creating site maps that describe
your website. Once you create a site map, you can use it with new navigation controls
to let users move comfortably around your website.
Master pages: With master pages, you can define a template and reuse it effortlessly. On a similar note, ASP.NET themes let you define a standardized set of appearance characteristics for controls, which you can apply across your website for a consistent look.
Data providers: With the new data provider model, you can extract information from a database and control how it’s displayed without writing a single line of code. ASP.NET 2.0 also adds new data controls that are designed to show information with much less hassle (either in a grid or in a browser view that shows a single record at a time).
Portals: One common type of web application is the portal, which centralizes different
information using separate panes on a single web page.
Administration: To configure an application in ASP.NET 1.x, you needed to edit a
configuration file by hand. Although this process wasn’t too difficult, ASP.NET 2.0
streamlines it with the WAT (Website Administration Tool), which works through a
web page interface.

CHAPTER 3
REQUIREMENT ANALYSIS
3.1 FUNCTIONAL REQUIREMENTS
In software engineering, a functional requirement defines a function of a software system or its module used. A function is defined as a set of inputs, the behavior, and outputs. Functional requirements may be calculations, technical details, data handling and processing and other specific functionality that define what a system is supposed to achieve. Behavioral requirements describing all the cases where the system uses the functional requirements are captured in use cases.
Here, the system has to do the following tasks:
• Take user id and password along with secret key, match it with corresponding database entries. If a match is found then continue else raise an error message.
• Encrypt the file to form a new encrypted file by using an encryption algorithm.
• Must be able to retrieve the original file from the encrypted file using the corresponding decryption algorithm.
• If any modification is performed on encrypted file, owner of the file should be notified.

3.2 NON-FUNCTIONAL REQUIREMENTS
In systems engineering and requirements engineering, a non-functional requirement is a requirement that specifies criteria that can be used to judge the operation of a system, rather than specific behaviors. This should be contrasted with functional requirements that define specific behavior or functions. The plan for implementing functional requirements is detailed in the system design. The plan for implementing non-functional requirements is detailed in the system architecture.
Other terms for non-functional requirements are “constraints”, “quality attributes”, “quality goals”, “quality of service requirements” and “non-behavioral requirements”.
Some of the quality attributes are as follows:
3.2.1 ACCESSIBILITY:
Accessibility is a general term used to describe the degree to which a product, device, service, or environment is accessible by as many people as possible.
In our project people who have registered with the cloud can access the cloud to store and retrieve their data with the help of a secret key sent to their email ids.
User interface is simple and efficient and easy to use.

3.2.2 MAINTAINABILITY:
In software engineering, maintainability is the ease with which a software product can be modified in order to:
• Correct defects
• Meet new requirements
New functionalities can be added in the project based on the user requirements just by adding the appropriate files to existing project using ASP.net and C# programming languages.
Since the programming is very simple, it is easier to find and correct the defects and to make the changes in the project.

3.2.3 SCALABILITY:
System is capable of handling increase total throughput under an increased load when resources (typically hardware) are added.
System can work normally under situations such as low bandwidth and large number of users.

3.2.4 PORTABILITY:
Portability is one of the key concepts of high-level programming. Portability is the software code base feature to be able to reuse the existing code instead of creating new code when moving software from an environment to another.
Project can be executed under different operation conditions provided it meet its minimum configurations. Only system files and dependant assemblies would have to be configured in such case.

3.3 HARDWARE REQUIREMENTS
• Processor : Dual Core Processor
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor :VGA and High Resolution Monitor.
• Input Device : Standard Keyboard and Mouse.
• Ram : 256 Mb.

3.4 SOFTWARE REQUIREMENTS
• Operating system : Windows 10/8/7/XP

• Front End : JAVA, Swing(JFC),RMIJ2ME

• Back End : MS-Access

• Tool : Eclipse

CHAPTER 4
DESIGN
4.1 DESIGN GOALS
To enable secure outsourcing of file under the aforementioned model, our mechanism design should achieve the following security and performance guarantees:
4.1.1 INPUT/OUTPUT PRIVACY
No sensitive information from the customer’s private data can be derived by the cloud server during performing the encryption and transfer.
4.1.2 EFFICIENCY
The local computations done by customer should be substantially less than. The computation burden on the cloud server should be within the comparable time complexity of existing practical algorithms for encryption and decryption of files.

4.2 SYSTEM ARCHITECTURE
Here the client sends the query to the server. Based on the query the server sends the corresponding file to the client. Then the client authorization is done by checking user id and password. In the server side, it checks the client name and its password for security process. If it is satisfied and then received the queries form the client and search the corresponding files in the database. Finally, find that file and send to the client. If the server finds the intruder means, it set the alternative Path to those intruders. If any intruders tries to access any file then multiple times password is asked to them and at last the intruders are directed to fake file. Here intruders will not know that the file he obtained is fake. They think that the file they got is original one.

Fig 4.1 System Architecture

4.3 DATA FLOW DIAGRAM
The Data Flow Diagram(DFD) is also named as bubble chart diagram which is a simple graphical representation that can be used to represent a system. The system representation is done in terms of the input data to the system, the various processing carried out on these data, and the output data is generated by the system.

Fig 4.2 Data Flow Diagram

4.4 SEQUENCE DIAGRAM
The sequence diagrams are an easy way of describing the system’s behavior. It focuses on the interaction between the system and the environment. This UML diagram shows the interaction arranged in a time sequence. It has two dimensions: the vertical dimension and the horizontal dimension. The vertical dimension used in UML sequence diagram represents the time and the horizontal dimension used represents the different objects. The vertical line is also called the object’s lifeline. It represents the object’s presence during the interaction.

Fig 4.3 Sequence Diagram

4.5 USE CASE DIAGRAM

A use-case diagram is a graph of users or actors. It is a set of use cases enclosed by a system boundary which is also the participation associations between the actors and the use-cases, and generalization among the use cases.
So, the use-case is the description of the outside (actors or users) and inside(use-case) of the system’s typical behavior. An ellipse having the name is used to show the use case which is initiated by actors or users.
.
An Actor or a user is the one that communicates with a use-case. Name of the actors is written down and a arrow symbol is used to show the interaction between actor and use-case.

Fig 4.4 Use Case Diagram

4.6 CLASS DIAGRAM

Fig 4.5 Class Diagram

4.7 ACTIVITY DIAGRAM
An activity diagram consists of numerous states that represent several operations. The transition from one state to the other is triggered by the completion of the operation. A round box having operation name is used in the diagram. For the execution of that operation, an operation symbol is used for indication. An activity diagram shows the inner state of an object.

Fig 4.6 Activity Diagram

CHAPTER 5
IMPLEMENTATION
Among the various stages of project, the part which converts the theoretical design into a working system is known as Implementation, thus making it one of the critical phase for developing a successful system.
In Implementation phase we carefully plan as well as probe the existing system keeping in mind the constraints of the implementation.

5.1 MAIN MODULES

5.1.1 CLIENT MODULE
In this module, the server receives a query sent from the client. Depending upon the query, the client is served the required files by the server. Before the server serves the request, authorization of client takes place. The server matches the client credentials for security. Only if it matches with the database the request is serviced and the corresponding file is served. If by any means, unauthorized user is detected redirection to the dummy file takes place.

5.1.2 SYSTEM MODULE

The above figure illustrates the network architecture of the cloud data .
Figure 1. Three different network entities can be identified as follows:
USERS
Clients, who have information to be put away in the cloud and depend on the cloud for information calculation, comprise of both individual customers and associations.

CLOUD SERVICE PROVIDER (CSP)
A CSP, is a person who has substantial assets and skills in structuring and supervising dispersed cloud storage hosts, possesses and controls live Cloud Computing systems,.

THIRD PARTY INSPECTOR(TPI)
A voluntary TPI, who expertise’s and abilities that consumers may not have, is
Trust worthy to evaluate and uncover hazard of cloud storage facilities on behalf of the consumers upon demand.

5.1.3 CLOUD DATA STORAGE MODULE

The user’s data is stored into cloud servers by the help of CSP, which are being processed in a successive manner, the user contact with the servers via CSP for accessing or retrieving his own data. In rare case scenarios, the user may feel the need for performing minute level modifications on the data. Users if provided with some security means so that they can perform data modifications on server level without the need of storing them on their own system. The optional TPI can be used for monitoring the data for the users who have trouble for maintaining time. In our purposed system, each and every communication between the user and the server is authenticated which provides reliability to our system.

5.1.4 CLOUD AUTHENTICATION SERVER
The Authentication Server (AS) implements functionality as most of the AS would with three levels of security in addition to the traditional client-authentication practice. In first addition the client authentication info is sent to the masked router. The AS used in this purposed system also has functionalities such as a ticketing personnel, regulatory approvals on the system network. The other functionalities may include such as updating of client lists, reducing client authentication time or revoking the access of a user.

5.1.5 UNAUTHORIZED DATA MODIFICATION AND CORRCORRUPTION MODULE
The important aspect of our purposed system is to prevent unauthorized access to the file which may result in data modification or rather corruption of data. Also it should be able to provide information regarding the unauthorized user like: time of access as well as the ip address of the unauthorized intruder.

5.1.6 ANTAGONIST MODULE
The threats can be originated from two different sources. A cloud service provider can have malicious intents who may move the data to a less secure storage and may also hide data losses which might occur due to several errors.
Also considering the other aspect, a person who possess the ability to compromise a number of cloud storage servers may perform data modification attacks while remaining undetected from the cloud service provider.

CHAPTER 6
TESTING
The purpose of testing is to discover errors. Testing is the process of trying to discover every conceivable fault or weakness in a work product. It provides a way to check the functionality of components, sub assemblies, assemblies and/or a finished product it is the process of exercising software with the intent of ensuring that the Software system meets its requirements and user expectations and does not fail in an unacceptable manner. There are various types of test. Each test type addresses a specific testing requirement.
TYPES OF TESTS
6.1 UNIT TESTING
Unit testing involves the design of test cases that validate that the internal program logic is functioning properly, and that program inputs produce valid outputs. All decision branches and internal code flow should be validated. It is the testing of individual software units of the application .it is done after the completion of an individual unit before integration. This is a structural testing, that relies on knowledge of its construction and is invasive. Unit tests perform basic tests at component level and test a specific business process, application, and/or system configuration. Unit tests ensure that each unique path of a business process performs accurately to the documented specifications and contains clearly defined inputs and expected results.

6.2 INTEGRATION TESTING
Integration tests are designed to test integrated software components to determine if they actually run as one program. Testing is event driven and is more concerned with the basic outcome of screens or fields. Integration tests demonstrate that although the components were individually satisfaction, as shown by successfully unit testing, the combination of components is correct and consistent. Integration testing is specifically aimed at exposing the problems that arise from the combination of components.

6.3 VALIDATION TESTING
An engineering validation test (EVT) is performed on first engineering prototypes, to ensure that the basic unit performs to design goals and specifications. It is important in identifying design problems, and solving them as early in the design cycle as possible, is the key to keeping projects on time and within budget. Too often, product design and performance problems are not detected until late in the product development cycle — when the product is ready to be shipped. The old adage holds true: It costs a penny to make a change in engineering, a dime in production and a dollar after a product is in the field.
Verification is a Quality control process that is used to evaluate whether or not a product, service, or system complies with regulations, specifications, or conditions imposed at the start of a development phase. Verification can be in development, scale-up, or production. This is often an internal process.
Validation is a Quality assurance process of establishing evidence that provides a high degree of assurance that a product, service, or system accomplishes its intended requirements. This often involves acceptance of fitness for purpose with end users and other product stakeholders.
The testing process overview is as follows:

Figure 6.1: The testing process

6.4 SYSTEM TESTING
System testing of software or hardware is testing conducted on a complete, integrated system to evaluate the system’s compliance with its specified requirements. System testing falls within the scope of black box testing, and as such, should require no knowledge of the inner design of the code or logic.
As a rule, system testing takes, as its input, all of the “integrated” software components that have successfully passed integration testing and also the software system itself integrated with any applicable hardware system(s).
System testing is a more limited type of testing; it seeks to detect defects both within the “inter-assemblages” and also within the system as a whole.
System testing is performed on the entire system in the context of a Functional Requirement Specification(s) (FRS) and/or a System Requirement Specification (SRS).
System testing tests not only the design, but also the behavior and even the believed expectations of the customer. It is also intended to test up to and beyond the bounds defined in the software/hardware requirements specification(s).

6.5 TESTING OF INITIALIZATION AND UI COMPONENTS

Serial Number of Test Case TC 01
Module Under Test DATABASE Connection
Description
When the client program is executed, it tries to connect to DATABASE (SQL server) using the data source and catalogue.
Output If the connection details are correct, the DATABASE is connected. If the connection details are incorrect, an exception is thrown.
Remarks Test Successful.

Table 6.1: Test case for connection setup

Serial Number of Test Case TC 02
Module Under Test User Registration
Description A page where users enter their details for registering themselves to the DATABASE server.
Input Details of Users such as first name, last name, age, mail id, etc…
Output If the user’s details are correct and matches the correct format, user is registered. If the user is already registered, an Exception is thrown.
Remarks Test Successful.

Table 6.2: Test Case for User Registration

Serial Number of Test Case TC 03
Module Under Test User Login
Description When the user tries to log in, details of user
Are verified with the DATABASE.
Input UserId and Password and secret key.
Output If the login details are correct, the user is logged in and user page is displayed. If the login details are incorrect.
Remarks Test Successful.

Table 6.3: Test Case for User Login

Serial Number of Test Case TC 04
Module Under Test File Upload
Description When the user submits the problem, problem is stored in the DATABASE after encryption.
Input User selects the file to be submitted.
Output If the file details are correct, the file is encrypted and stored in DATABASE. A security key is sent to owner’s mail for verification.
Remarks Test Successful.

Table 6.4: Test Case for File Upload

Serial Number of Test Case TC 05
Module Under Test Secret Key Verification
Description When the user enters the Secret Key for login or his submitted file, it is verified with the server.
Input Secret key
Output If the secret Key value matches with that stored in the DATABASE, User can verifies the content and can grant permission for download. If the secret Key value doesn’t match, a message is displayed.
Remarks Test Successful.

Table 6.5: Test Case for Verifying Secret Key

Serial Number of Test Case TC 06
Module Under Test File modification
Description When unauthorised user changes the content of file
Output Message from the Admin
Remarks Test Successful.

Table 6.6: Test Case for Modification performed

CHAPTER 1
INTRODUCTION
1.1 INFORMATION CONSISTENCY
Numerous developments are being introduced as the epoch of Cloud Computing, which is an cyberspace-based progress and use of supercomputer expertise. Most powerful processors which were too expensive to begin with have become cheaper by the help of pooling processing power and providing the processing power on demand. The development of high speed internet with increased bandwidth have increased the quality of services leading to better customer satisfaction which the most primitive goal of any organization.
The migration of data from the users’ computer to the remote data centers have the provided the customer with great and reliable convenience. Amazon simple storage services are the well-known examples which are one of the pioneers of cloud services. The eliminate the need of maintain the data on a local system which is a huge boost for increasing quality of service. But due to this the customers are always as the mercifulness of the cloud service provider as their downtime causes the user to be unable to access his own data. Since every coin has two sides, likewise cloud computing has its own fair share of security threats and also there may be some threats which are yet to be discovered. Considering from the user’s point of view, he wants his data to be secure therefore, data security is the most important aspect which will ultimately lead to the customer satisfaction. The users’ have limited control on their own data so the conventional cryptography measures cannot be adopted. Thus, the data stored on the cloud should be verified occasionally to ensure the data has not been modified without informing the owner. The data which is rarely used is sometimes moved to lower tier storage making it more vulnerable for attacks. On the other note, Cloud Computing not only stores the data but also provides the user with functionality like modifying the data, appending some information to it or permanently deleting the data. To assure the integrity of data various hashing algorithms can be used to create checksums which will alert the user about the data modifications.

1.2 PROBLEM DEFINITION
Firstly, traditional cryptographic primitives for the purpose of data security protection cannot be directly adopted due to the users’ loss control of data under Cloud Computing. Whenever it comes to the matter relating to cloud services the user is put at a disadvantage regarding to the security of the file. Basically the file is stored on a server which is a pool resource that is any one with user’s credentials can access the file and if in case the attacker comes to know about the password as well as the encryption keys the attacker can modify the file contents, thus making the information stored in the file to be accessed by the unauthorized user. So, the problem is that what if someone copy’s your work and claims to be his own work. Anything we design , anything we invent is governed by the principle of whether or not it guarantees customer satisfaction.
Hence, the problem is underlying whether the customer can rest assured that his data is safe from unauthorized access or not.

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1.3 PROJECT PURPOSE
In our purposed system, we provide assurance to the user that his information is safe by “implementing a system which provides security mechanisms by offering three levels of security”. Concerning about the data security part, our system is divided mainly into three modules named “IP triggering” module, “client-authentication” module and “redirecting” module. The system generates a user password and a key which is used for client authentication.
The algorithm generates two keywords 8 bit length consisting of combinations of characters, special characters, and numbers which is used for client authorization and file authorization.
Questions may arise as why do we use keys of 8 bit length only? The purpose of our system is to prevent illegal data access if the users’ credential are compromised. By testing against weak algorithms which are easier to crack we design our system to be more robust.

1.4 PROJECT FEATURES
Our scheme would be to prevent illegal access of users’ data. A user after getting himself registered on the system will have the advantage of different layers of security. The most primitive work our system is to inform the user that his data has been accessed from an unregistered ip by using mail triggering events. For login, the attacker tries to access the file by using the credentials stolen from the victim, and upon entering is provided with a dialog box to enter a key. The attacker tries to enter the key which won’t be accepted by any means. The attacker is provided with a three tries so that he can go back. After 3 tries, the attacker is provided with the access of the fake file which is implemented by the redirection module.

1.5 MODULES DESCRIPTION
1.5.1 CLOUD STORAGE
Data outsourcing to cloud storage servers is raising trend among many firms and users owing to its economic advantages. This essentially means that the owner (client) of the data moves its data to a third party cloud storage server which is supposed to – presumably for a fee – faithfully store the data with it and provide it back to the owner whenever required. Cloud storage increases maintainability and decreases storage cost associated with storage.

1.5.2 SIMPLY ARCHIVES
This problem tries to obtain and verify a proof that the data that is stored by a user at remote data storage in the cloud (called cloud storage archives or simply archives) is not modified by the archive and thereby the integrity of the data is assured.
The file is encrypted using symmetric key algorithms ( same key is used for encryption and decryption of data) before storing it in cloud storage. Cloud archive is not cheating the owner, if cheating, in this context, means that the storage archive might delete some of the data or may modify some of the data.
While developing proofs for data possession at untrusted cloud storage servers we are often limited by the resources at the cloud server as well as at the client.

1.5.3 SENTINELS
In this scheme, unlike in the key-hash approach scheme, only a single key can be used irrespective of the size of the file or the number of files whose retrievability it wants to verify. Also the archive needs to access only a small portion of the file F unlike in the key-has scheme which required the archive to process the entire file F for each protocol verification. If the prover has modified or deleted a substantial portion of F, then with high probability it will also have suppressed a number of sentinels.

1.5.4 VERIFICATION PHASE:
The verifier before storing the file at the archive preprocesses the file and appends some Meta data to the file and stores at the archive. At the time of verification the verifier uses this Meta data to verify the integrity of the data. If the metadata matches the already stored metadata in database then there is inconsistency in file and user user is alerted with a warning message.l It is important to note that our proof of information consistency protocol just checks the integrity of data i.e. if the data has been illegally modified or deleted. It does not prevent the archive from modifying the data.

CHAPTER 2
LITERATURE SURVEY
2.1 CLOUD COMPUTING
Literature survey is the most important step in software development process. Before developing the tool it is necessary to determine the time factor, economy and company strength. Once these things are satisfied, then next steps is to determine which operating system and language can be used for developing the tool. Once the programmers start building the tool the programmers need lot of external support. This support can be obtained from senior programmers, from book or from websites. Before building the system the above consideration are taken into account for developing the proposed system. We have to analysis the Cloud Computing Outline Survey:
Cloud Computing
Cloud computing providing unlimited infrastructure to store and execute customer data and program. As customers you do not need to own the infrastructure, they are merely accessing or renting; they can forego capital expenditure and consume resources as a service, paying instead for what they use.
Instead of running programs and data on an individual desktop computer, everything is hosted in the “cloud”—a nebulous assemblage of computers and servers accessed via the Internet. Cloud computing lets you access all your applications and documents from anywhere in the world, freeing you from the confines of the desktop and making it easier for group members in different locations to collaborate.
In short, cloud computing enables a shift from the computer to the user, from applications to tasks, and from isolated data to data that can be accessed from anywhere and shared with anyone. The user no longer has to take on the task of data management; he doesn’t even have to remember where the data is. All that matters is that the data is in the cloud, and thus immediately available to that user and to other authorized users.

Benefits of Cloud Computing:
• Minimized Capital expenditure
• Location and Device independence
• Utilization and efficiency improvement
• Very high Scalability
• High Computing power
How secure is encryption Scheme:
• Is it possible for all of my data to be fully encrypted?
• What algorithms are used?
• Who holds, maintains and issues the keys?
• Encryption accidents can make data totally unusable.
• Encryption can complicate availability Solution

2.2 EXISTING SYSTEM
As data generation is far outpacing data storage it proves costly for small firms to frequently update their hardware whenever additional data is created. Also maintaining the storages can be a difficult task. It transmitting the file across the network to the client can consume heavy bandwidths. The problem is further complicated by the fact that the owner of the data may be a small device, like a PDA (personal digital assist) or a mobile phone, which have limited CPU power, battery power and communication bandwidth.
Disadvantages:
• The main drawback of this scheme is the high resource costs it requires for the implementation.
• Also computing hash value for even a moderately large data files can be computationally burdensome for some clients (PDAs, mobile phones, etc).
• Data encryption is large so the disadvantage is small users with limited computational power (PDAs, mobile phones etc.).
• Consumption of large amount of bandwidth in transmission of file.

2.3 PROPOSED SYSTEM
One of the important concerns that need to be addressed is to assure the customer of the integrity i.e. correctness of his data in the cloud. As the data is physically not accessible to the user the cloud should provide a way for the user to check if the integrity of his data is maintained or is compromised. In this paper we provide a scheme which gives a proof of data integrity in the cloud which the customer can employ to check the correctness of his data in the cloud. This proof can be agreed upon by both the cloud and the customer and can be incorporated in the Service level agreement (SLA). It is important to note that our proof of data integrity protocol just checks the integrity of data i.e. if the data has been illegally modified or deleted.
Advantages:
? Apart from reduction in storage costs data outsourcing to the cloud also helps in reducing the maintenance.
? Avoiding local storage of data.
? By reducing the costs of storage, maintenance and personnel.
? It reduces the chance of losing data by hardware failures.
? Not cheating the owner.

2.4 SOFTWARE DESCRIPTION
2.4.1 C#
C# (pronounced see sharp) is a multi-paradigm programming language encompassing strong typing, imperative, declarative, functional, generic, object-oriented (class-based), and component-oriented programming disciplines. It was developed by Microsoft within its .NET initiative and later approved as a standard by Ecma (ECMA-334) and ISO (ISO/IEC 23270:2006). C# is one of the programming languages designed for the Common Language Infrastructure. Support for internationalization is very important.

The ECMA standard lists the design goals for C# as:
• C# language is intended to be a simple, modern, general-purpose, object-oriented programming language.
• The language, and implementations thereof, should provide support for software engineering principles such as strong type checking, array bounds checking, detection of attempts to use uninitialized variables, and automatic garbage collection. Software robustness, durability, and programmer productivity are important.
• The language is intended for use in developing software components suitable for deployment in distributed environments.
• Source code portability is very important, as is programmer portability, especially for those programmers already familiar with C and C++.
• C# is intended to be suitable for writing applications for both hosted and embedded systems, ranging from the very large that use sophisticated operating systems, down to the very small having dedicated functions.
• Although C# applications are intended to be economical with regard to memory and processing power requirements, the language was not intended to compete directly on performance and size with C or assembly language.

2.4.2 .NET FRAMWORK PLATFORM ARCHITECTURE
Microsoft .NET is a set of Microsoft software technologies for rapidly building and integrating XML Web services, Microsoft Windows-based applications, and Web solutions. The .NET Framework is a language-neutral platform for writing programs that can easily and securely interoperate. The .NET framework provides the foundation for components to interact seamlessly, whether locally or remotely on different platforms. It standardizes common data types and communications protocols so that components created in different languages can easily interoperate.

ASP.NET
XML WEB SERVICES Windows Forms
Base Class Libraries
Common Language Runtime
Operating System

Fig 2.1 NET Framework Architecture
The .NET Framework has two main parts:
1. The Common Language Runtime (CLR).
2. A hierarchical set of class libraries.
The CLR is described as the “execution engine” of .NET. It provides the environment within which programs run. The most important features are:
• Conversion from a low-level assembler-style language, called Intermediate Language (IL), into code native to the platform being executed on.
• Memory management, notably including garbage collection.
• Checking and enforcing security restrictions on the running code.
• Loading and executing programs, with version control and other such features.

Common Type System
The CLR uses something called the Common Type System (CTS) to strictly enforce type-safety. This ensures that all classes are compatible with each other, by describing types in a common way. CTS define how types work within the runtime, which enables types in one language to interoperate with types in another language, including cross-language exception handling.
As well as ensuring that types are only used in appropriate ways, the runtime also ensures that code doesn’t attempt to access memory that hasn’t been allocated to it.

Common Language Specification
The CLR provides built-in support for language interoperability. To ensure that you can develop managed code that can be fully used by developers using any programming language, a set of language features and rules for using them called the Common Language Specification (CLS) has been defined. Components that follow these rules and expose only CLS features are considered CLS-compliant.

THE CLASS LIBRARY
.NET provides a single-rooted hierarchy of classes, containing over 7000 types. The root of the namespace is called System; this contains basic types like Byte, Double, Boolean, and String, as well as Object. All objects derive from System. Object.
As well as objects, there are value types. Value types can be allocated on the stack, which can provide useful flexibility. There are also efficient means of converting value types to object types if and when necessary.

2.4.3 SQL-SERVER
The OLAP Services feature available in SQL Server version 7.0 is now called SQL Server 2000 Analysis Services. The term OLAP Services has been replaced with the term Analysis Services. Analysis Services also includes a new data mining component. The Repository component available in SQL Server version 7.0 is now called Microsoft SQL Server 2000 Meta Data Services. References to the component now use the term Meta Data Services. The term repository is used only in reference to the repository engine within Meta Data Services SQL-SERVER database consist of following type of objects:
1. TABLE
2. QUERY
3. FORM
4. REPORT
5. MACRO
TABLE:
A database is a collection of data about a specific topic.

VIEWS OF TABLE:
We can work with a table in two types,
1. Design View
2. Datasheet View
Design View
To build or modify the structure of a table we work in the table design view. We can specify what kind of data will be hold.
Datasheet View
To add, edit or analyses the data itself we work in tables datasheet view mode.
QUERY:
A query is a question that has to be asked the data. Access gathers data that answers the question from one or more table. The data that make up the answer is either dynaset (if you edit it) or a snapshot (it cannot be edited).
2.4.4 Jscript
JScript is Microsoft ‘s extended implementation of ECMAScript (ECMA262), an international standard based on Netscape’s JavaScript and Microsoft’s JScript languages. JScript is implemented as a Windows Script engine. This means that it can be “plugged in” to any application that supports Windows Script, such as Internet Explorer, Active Server Pages, and Windows Script Host. It also means that any application supporting Windows Script can use multiple languages – JScript, VBScript, Perl, and others.
JScript (and the other languages) can be used for both simple tasks (such as mouseovers on Web pages) and for more complex tasks (such as updating a database with ASP or running logon scripts for Windows NT ).
Windows Script relies on external “object models” to carry out much of its work. For example, Internet Explorer’s DOM provides objects such as ‘document’ and methods such as ‘write()’ to enable the scripting of Web pages.
JScript supports conditional compilation, which allows a programmer to selectively execute code within block comments. This is an extension to the ECMAScript standard that is not supported in other JavaScript implementations.
2.4.5 ASP or ACTIVE SERVER PAGES
Active Server Pages (ASP), also known as Classic ASP, was introduced in 1998 as Microsoft’s first server side scripting engine. ASP is a technology that enables scripts in web pages to be executed by an Internet server. ASP pages have the file extension .asp, and are normally written in VBScript. ASP.NET is a development framework for building web pages and web sites with HTML, CSS, JavaScript and server scripting.
When a browser requests an ASP file, the ASP.NET engine reads the file, compiles and executes the scripts in the file, and returns the result to the browser as plain HTML. ASP.NET supports three different development models:
Web Pages, MVC (Model View Controller), and Web Forms:
Web Pages
Single Pages Model MVC
Model View Controller Web Forms
Event Driven Model

1. Simplest ASP.NET model.
2. Similar to PHP and classic ASP.
3. Built-in templates and helpers for database, video, graphics, social media and more.
1. MVC separates web applications into 3 different components.
2. Models for data
Views for display
Controllers for input
1.Traditional ASP.NET event driven development model.
2. Web pages with added server controls, server events, and server code.
Fig 2.2 Development Models for ASP.NET

ASP.NET
ASP.NET is a new ASP generation. It is not compatible with Classic ASP, but ASP.NET may include Classic ASP. ASP.NET pages are compiled, which makes them faster than Classic ASP. ASP.NET has better language support, a large set of user controls, XML-based components, and integrated user authentication.
ASP.NET pages have the extension .aspx, and are normally written in VB (Visual Basic) or C# (C sharp). User controls in ASP.NET can be written in different languages, including C++ and Java.
Here are highlights of some of the new features:
Navigation: ASP.NET has a new higher-level model for creating site maps that describe
your website. Once you create a site map, you can use it with new navigation controls
to let users move comfortably around your website.
Master pages: With master pages, you can define a template and reuse it effortlessly. On a similar note, ASP.NET themes let you define a standardized set of appearance characteristics for controls, which you can apply across your website for a consistent look.
Data providers: With the new data provider model, you can extract information from a database and control how it’s displayed without writing a single line of code. ASP.NET 2.0 also adds new data controls that are designed to show information with much less hassle (either in a grid or in a browser view that shows a single record at a time).
Portals: One common type of web application is the portal, which centralizes different
information using separate panes on a single web page.
Administration: To configure an application in ASP.NET 1.x, you needed to edit a
configuration file by hand. Although this process wasn’t too difficult, ASP.NET 2.0
streamlines it with the WAT (Website Administration Tool), which works through a
web page interface.

CHAPTER 3
REQUIREMENT ANALYSIS
3.1 FUNCTIONAL REQUIREMENTS
In software engineering, a functional requirement defines a function of a software system or its module used. A function is defined as a set of inputs, the behavior, and outputs. Functional requirements may be calculations, technical details, data handling and processing and other specific functionality that define what a system is supposed to achieve. Behavioral requirements describing all the cases where the system uses the functional requirements are captured in use cases.
Here, the system has to do the following tasks:
• Take user id and password along with secret key, match it with corresponding database entries. If a match is found then continue else raise an error message.
• Encrypt the file to form a new encrypted file by using an encryption algorithm.
• Must be able to retrieve the original file from the encrypted file using the corresponding decryption algorithm.
• If any modification is performed on encrypted file, owner of the file should be notified.

3.2 NON-FUNCTIONAL REQUIREMENTS
In systems engineering and requirements engineering, a non-functional requirement is a requirement that specifies criteria that can be used to judge the operation of a system, rather than specific behaviors. This should be contrasted with functional requirements that define specific behavior or functions. The plan for implementing functional requirements is detailed in the system design. The plan for implementing non-functional requirements is detailed in the system architecture.
Other terms for non-functional requirements are “constraints”, “quality attributes”, “quality goals”, “quality of service requirements” and “non-behavioral requirements”.
Some of the quality attributes are as follows:
3.2.1 ACCESSIBILITY:
Accessibility is a general term used to describe the degree to which a product, device, service, or environment is accessible by as many people as possible.
In our project people who have registered with the cloud can access the cloud to store and retrieve their data with the help of a secret key sent to their email ids.
User interface is simple and efficient and easy to use.

3.2.2 MAINTAINABILITY:
In software engineering, maintainability is the ease with which a software product can be modified in order to:
• Correct defects
• Meet new requirements
New functionalities can be added in the project based on the user requirements just by adding the appropriate files to existing project using ASP.net and C# programming languages.
Since the programming is very simple, it is easier to find and correct the defects and to make the changes in the project.

3.2.3 SCALABILITY:
System is capable of handling increase total throughput under an increased load when resources (typically hardware) are added.
System can work normally under situations such as low bandwidth and large number of users.

3.2.4 PORTABILITY:
Portability is one of the key concepts of high-level programming. Portability is the software code base feature to be able to reuse the existing code instead of creating new code when moving software from an environment to another.
Project can be executed under different operation conditions provided it meet its minimum configurations. Only system files and dependant assemblies would have to be configured in such case.

3.3 HARDWARE REQUIREMENTS
• Processor : Dual Core Processor
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor :VGA and High Resolution Monitor.
• Input Device : Standard Keyboard and Mouse.
• Ram : 256 Mb.

3.4 SOFTWARE REQUIREMENTS
• Operating system : Windows 10/8/7/XP

• Front End : JAVA, Swing(JFC),RMIJ2ME

• Back End : MS-Access

• Tool : Eclipse

CHAPTER 4
DESIGN
4.1 DESIGN GOALS
To enable secure outsourcing of file under the aforementioned model, our mechanism design should achieve the following security and performance guarantees:
4.1.1 INPUT/OUTPUT PRIVACY
No sensitive information from the customer’s private data can be derived by the cloud server during performing the encryption and transfer.
4.1.2 EFFICIENCY
The local computations done by customer should be substantially less than. The computation burden on the cloud server should be within the comparable time complexity of existing practical algorithms for encryption and decryption of files.

4.2 SYSTEM ARCHITECTURE
Here the client sends the query to the server. Based on the query the server sends the corresponding file to the client. Then the client authorization is done by checking user id and password. In the server side, it checks the client name and its password for security process. If it is satisfied and then received the queries form the client and search the corresponding files in the database. Finally, find that file and send to the client. If the server finds the intruder means, it set the alternative Path to those intruders. If any intruders tries to access any file then multiple times password is asked to them and at last the intruders are directed to fake file. Here intruders will not know that the file he obtained is fake. They think that the file they got is original one.

Fig 4.1 System Architecture

4.3 DATA FLOW DIAGRAM
The Data Flow Diagram(DFD) is also named as bubble chart diagram which is a simple graphical representation that can be used to represent a system. The system representation is done in terms of the input data to the system, the various processing carried out on these data, and the output data is generated by the system.

Fig 4.2 Data Flow Diagram

4.4 SEQUENCE DIAGRAM
The sequence diagrams are an easy way of describing the system’s behavior. It focuses on the interaction between the system and the environment. This UML diagram shows the interaction arranged in a time sequence. It has two dimensions: the vertical dimension and the horizontal dimension. The vertical dimension used in UML sequence diagram represents the time and the horizontal dimension used represents the different objects. The vertical line is also called the object’s lifeline. It represents the object’s presence during the interaction.

Fig 4.3 Sequence Diagram

4.5 USE CASE DIAGRAM

A use-case diagram is a graph of users or actors. It is a set of use cases enclosed by a system boundary which is also the participation associations between the actors and the use-cases, and generalization among the use cases.
So, the use-case is the description of the outside (actors or users) and inside(use-case) of the system’s typical behavior. An ellipse having the name is used to show the use case which is initiated by actors or users.
.
An Actor or a user is the one that communicates with a use-case. Name of the actors is written down and a arrow symbol is used to show the interaction between actor and use-case.

Fig 4.4 Use Case Diagram

4.6 CLASS DIAGRAM

Fig 4.5 Class Diagram

4.7 ACTIVITY DIAGRAM
An activity diagram consists of numerous states that represent several operations. The transition from one state to the other is triggered by the completion of the operation. A round box having operation name is used in the diagram. For the execution of that operation, an operation symbol is used for indication. An activity diagram shows the inner state of an object.

Fig 4.6 Activity Diagram

CHAPTER 5
IMPLEMENTATION
Among the various stages of project, the part which converts the theoretical design into a working system is known as Implementation, thus making it one of the critical phase for developing a successful system.
In Implementation phase we carefully plan as well as probe the existing system keeping in mind the constraints of the implementation.

5.1 MAIN MODULES

5.1.1 CLIENT MODULE
In this module, the server receives a query sent from the client. Depending upon the query, the client is served the required files by the server. Before the server serves the request, authorization of client takes place. The server matches the client credentials for security. Only if it matches with the database the request is serviced and the corresponding file is served. If by any means, unauthorized user is detected redirection to the dummy file takes place.

5.1.2 SYSTEM MODULE

The above figure illustrates the network architecture of the cloud data .
Figure 1. Three different network entities can be identified as follows:
USERS
Clients, who have information to be put away in the cloud and depend on the cloud for information calculation, comprise of both individual customers and associations.

CLOUD SERVICE PROVIDER (CSP)
A CSP, is a person who has substantial assets and skills in structuring and supervising dispersed cloud storage hosts, possesses and controls live Cloud Computing systems,.

THIRD PARTY INSPECTOR(TPI)
A voluntary TPI, who expertise’s and abilities that consumers may not have, is
Trust worthy to evaluate and uncover hazard of cloud storage facilities on behalf of the consumers upon demand.

5.1.3 CLOUD DATA STORAGE MODULE

The user’s data is stored into cloud servers by the help of CSP, which are being processed in a successive manner, the user contact with the servers via CSP for accessing or retrieving his own data. In rare case scenarios, the user may feel the need for performing minute level modifications on the data. Users if provided with some security means so that they can perform data modifications on server level without the need of storing them on their own system. The optional TPI can be used for monitoring the data for the users who have trouble for maintaining time. In our purposed system, each and every communication between the user and the server is authenticated which provides reliability to our system.

5.1.4 CLOUD AUTHENTICATION SERVER
The Authentication Server (AS) implements functionality as most of the AS would with three levels of security in addition to the traditional client-authentication practice. In first addition the client authentication info is sent to the masked router. The AS used in this purposed system also has functionalities such as a ticketing personnel, regulatory approvals on the system network. The other functionalities may include such as updating of client lists, reducing client authentication time or revoking the access of a user.

5.1.5 UNAUTHORIZED DATA MODIFICATION AND CORRCORRUPTION MODULE
The important aspect of our purposed system is to prevent unauthorized access to the file which may result in data modification or rather corruption of data. Also it should be able to provide information regarding the unauthorized user like: time of access as well as the ip address of the unauthorized intruder.

5.1.6 ANTAGONIST MODULE
The threats can be originated from two different sources. A cloud service provider can have malicious intents who may move the data to a less secure storage and may also hide data losses which might occur due to several errors.
Also considering the other aspect, a person who possess the ability to compromise a number of cloud storage servers may perform data modification attacks while remaining undetected from the cloud service provider.

CHAPTER 6
TESTING
The purpose of testing is to discover errors. Testing is the process of trying to discover every conceivable fault or weakness in a work product. It provides a way to check the functionality of components, sub assemblies, assemblies and/or a finished product it is the process of exercising software with the intent of ensuring that the Software system meets its requirements and user expectations and does not fail in an unacceptable manner. There are various types of test. Each test type addresses a specific testing requirement.
TYPES OF TESTS
6.1 UNIT TESTING
Unit testing involves the design of test cases that validate that the internal program logic is functioning properly, and that program inputs produce valid outputs. All decision branches and internal code flow should be validated. It is the testing of individual software units of the application .it is done after the completion of an individual unit before integration. This is a structural testing, that relies on knowledge of its construction and is invasive. Unit tests perform basic tests at component level and test a specific business process, application, and/or system configuration. Unit tests ensure that each unique path of a business process performs accurately to the documented specifications and contains clearly defined inputs and expected results.

6.2 INTEGRATION TESTING
Integration tests are designed to test integrated software components to determine if they actually run as one program. Testing is event driven and is more concerned with the basic outcome of screens or fields. Integration tests demonstrate that although the components were individually satisfaction, as shown by successfully unit testing, the combination of components is correct and consistent. Integration testing is specifically aimed at exposing the problems that arise from the combination of components.

6.3 VALIDATION TESTING
An engineering validation test (EVT) is performed on first engineering prototypes, to ensure that the basic unit performs to design goals and specifications. It is important in identifying design problems, and solving them as early in the design cycle as possible, is the key to keeping projects on time and within budget. Too often, product design and performance problems are not detected until late in the product development cycle — when the product is ready to be shipped. The old adage holds true: It costs a penny to make a change in engineering, a dime in production and a dollar after a product is in the field.
Verification is a Quality control process that is used to evaluate whether or not a product, service, or system complies with regulations, specifications, or conditions imposed at the start of a development phase. Verification can be in development, scale-up, or production. This is often an internal process.
Validation is a Quality assurance process of establishing evidence that provides a high degree of assurance that a product, service, or system accomplishes its intended requirements. This often involves acceptance of fitness for purpose with end users and other product stakeholders.
The testing process overview is as follows:

Figure 6.1: The testing process

6.4 SYSTEM TESTING
System testing of software or hardware is testing conducted on a complete, integrated system to evaluate the system’s compliance with its specified requirements. System testing falls within the scope of black box testing, and as such, should require no knowledge of the inner design of the code or logic.
As a rule, system testing takes, as its input, all of the “integrated” software components that have successfully passed integration testing and also the software system itself integrated with any applicable hardware system(s).
System testing is a more limited type of testing; it seeks to detect defects both within the “inter-assemblages” and also within the system as a whole.
System testing is performed on the entire system in the context of a Functional Requirement Specification(s) (FRS) and/or a System Requirement Specification (SRS).
System testing tests not only the design, but also the behavior and even the believed expectations of the customer. It is also intended to test up to and beyond the bounds defined in the software/hardware requirements specification(s).

6.5 TESTING OF INITIALIZATION AND UI COMPONENTS

Serial Number of Test Case TC 01
Module Under Test DATABASE Connection
Description
When the client program is executed, it tries to connect to DATABASE (SQL server) using the data source and catalogue.
Output If the connection details are correct, the DATABASE is connected. If the connection details are incorrect, an exception is thrown.
Remarks Test Successful.

Table 6.1: Test case for connection setup

Serial Number of Test Case TC 02
Module Under Test User Registration
Description A page where users enter their details for registering themselves to the DATABASE server.
Input Details of Users such as first name, last name, age, mail id, etc…
Output If the user’s details are correct and matches the correct format, user is registered. If the user is already registered, an Exception is thrown.
Remarks Test Successful.

Table 6.2: Test Case for User Registration

Serial Number of Test Case TC 03
Module Under Test User Login
Description When the user tries to log in, details of user
Are verified with the DATABASE.
Input UserId and Password and secret key.
Output If the login details are correct, the user is logged in and user page is displayed. If the login details are incorrect.
Remarks Test Successful.

Table 6.3: Test Case for User Login

Serial Number of Test Case TC 04
Module Under Test File Upload
Description When the user submits the problem, problem is stored in the DATABASE after encryption.
Input User selects the file to be submitted.
Output If the file details are correct, the file is encrypted and stored in DATABASE. A security key is sent to owner’s mail for verification.
Remarks Test Successful.

Table 6.4: Test Case for File Upload

Serial Number of Test Case TC 05
Module Under Test Secret Key Verification
Description When the user enters the Secret Key for login or his submitted file, it is verified with the server.
Input Secret key
Output If the secret Key value matches with that stored in the DATABASE, User can verifies the content and can grant permission for download. If the secret Key value doesn’t match, a message is displayed.
Remarks Test Successful.

Table 6.5: Test Case for Verifying Secret Key

Serial Number of Test Case TC 06
Module Under Test File modification
Description When unauthorised user changes the content of file
Output Message from the Admin
Remarks Test Successful.

Table 6.6: Test Case for Modification performed

Chapter 1
Introduction

1.1 Introduction
Mehsana city is one of the important city in the north Gujarat. Number of people come to Mehsana for the job aspect and education aspect from the neighbor’s city like patan and palanpur. So that parking is very important factor in Mehsana city. So that One of the problem created by road traffic is parking. Not only do vehicle required street space to move about, but also do they require space to park where to park where the occupants can be loaded and unloaded. Parking system is very important for the transportation system in India. So it can be design by two major method which are on-street parking and off-street parking. In now a day in India vehicle culture are fast growing so that it create a lack of parking spaces in cities. So it can make a one of the biggest problem in the city. The most of parking and traffic problem in city which making a main CBD area like a shopping mall, bazar. Now a day major cities accepted the smart future parking system like a multy story parking, underground parking, roof parking so it could be helpful to control the parking problem.Parking control has become the chief means available to cities all over the world do limit congestion. It is the enforcement of laws and regulations. The size of average parking space is 14m2. This result in a great demand for parking space.in the CBD and other area where the other acitivites are concentrated. Parking should be control by below method which are:-
There are two type of parking system
1 On-street Parking
2 Off street parking
On street parking
On street parking means the vehicles are parked on the sides of the street itself. This will be usually controlled by government agencies itself. Common types of on-street parking are as listed below. This classification is based on the angle in which the vehicles are parked with respect to the road alignment. As per IRC the standard dimensions of a car is taken as 5× 2.5 meters and that for a truck is 3.75× 7.5 meters.
1. Parallel parking: The vehicles are parked along the length of the road. Here there is no backward movement involved while parking or unparking the vehicle. Hence, it is the most safest parking from the accident perspective. However, it consumes the maximum curb length and therefore only a minimum number of vehicles can be parked for a given kerb length. This method of parking produces least obstruction to the on-going traffic on the road since least road width is used. The length available to park N number of vehicles, L = N 5.9
2. 30? parking: In thirty degree parking, the vehicles are parked at 30? with respect to the road alignment. In this case, more vehicles can be parked compared to parallel parking.
3. 45? parking: As the angle of parking increases, more number of vehicles can be parked. Hence compared to parallel parking and thirty degree parking, more number of vehicles can be accommodated in, length of parking space available for parking N number of vehicles in a given kerb is L = 3.54 N+1.77
4. 60? parking: The vehicles are parked at 60? to the direction of road. More number of vehicles can be accommodated, length available for parking N vehicles =2.89N+2.16.
5. Right angle parking: In right angle parking or 90? parking, the vehicles are parked perpendicular to the direction of the road. Although it consumes maximum width kerb length required is very little. In this type of parking, the vehicles need complex maneuvering and this may cause severe accidents. This arrangement causes obstruction to the road traffic particularly if the road width is less. However, it can accommodate maximum number of vehicles for a given kerb length. Length available for parking N number of vehicles is L = 2.5N.
Off street parking
In many urban centers, some areas are exclusively allotted for parking which will be at some distance away from the main stream of traffic. Such a parking is referred to as off-street. Off-street parking means parking your vehicle anywhere but on the streets. These are usually parking facilities like garages and lots. Off-street parking can be both indoors and outdoors. Off-street parking also includes private lots, garages and driveways. The users of on-street parking are casual users who use the space for a short period of time. Off street parking users differ from short to long-term, i.e. monthly tenants and regular users. In many urban centers, some areas are exclusively allotted for parking which will be at some distance away from the mainstream of traffic. Such a parking is referred to as off-street parking. They may be operated by either public agencies or private firms.

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Chapter 1
Introduction

1.1 Introduction
Mehsana city is one of the important city in the north Gujarat. Number of people come to Mehsana for the job aspect and education aspect from the neighbor’s city like patan and palanpur. So that parking is very important factor in Mehsana city. So that One of the problem created by road traffic is parking. Not only do vehicle required street space to move about, but also do they require space to park where to park where the occupants can be loaded and unloaded. Parking system is very important for the transportation system in India. So it can be design by two major method which are on-street parking and off-street parking. In now a day in India vehicle culture are fast growing so that it create a lack of parking spaces in cities. So it can make a one of the biggest problem in the city. The most of parking and traffic problem in city which making a main CBD area like a shopping mall, bazar. Now a day major cities accepted the smart future parking system like a multy story parking, underground parking, roof parking so it could be helpful to control the parking problem.Parking control has become the chief means available to cities all over the world do limit congestion. It is the enforcement of laws and regulations. The size of average parking space is 14m2. This result in a great demand for parking space.in the CBD and other area where the other acitivites are concentrated. Parking should be control by below method which are:-
There are two type of parking system
1 On-street Parking
2 Off street parking
On street parking
On street parking means the vehicles are parked on the sides of the street itself. This will be usually controlled by government agencies itself. Common types of on-street parking are as listed below. This classification is based on the angle in which the vehicles are parked with respect to the road alignment. As per IRC the standard dimensions of a car is taken as 5× 2.5 meters and that for a truck is 3.75× 7.5 meters.
1. Parallel parking: The vehicles are parked along the length of the road. Here there is no backward movement involved while parking or unparking the vehicle. Hence, it is the most safest parking from the accident perspective. However, it consumes the maximum curb length and therefore only a minimum number of vehicles can be parked for a given kerb length. This method of parking produces least obstruction to the on-going traffic on the road since least road width is used. The length available to park N number of vehicles, L = N 5.9
2. 30? parking: In thirty degree parking, the vehicles are parked at 30? with respect to the road alignment. In this case, more vehicles can be parked compared to parallel parking.
3. 45? parking: As the angle of parking increases, more number of vehicles can be parked. Hence compared to parallel parking and thirty degree parking, more number of vehicles can be accommodated in, length of parking space available for parking N number of vehicles in a given kerb is L = 3.54 N+1.77
4. 60? parking: The vehicles are parked at 60? to the direction of road. More number of vehicles can be accommodated, length available for parking N vehicles =2.89N+2.16.
5. Right angle parking: In right angle parking or 90? parking, the vehicles are parked perpendicular to the direction of the road. Although it consumes maximum width kerb length required is very little. In this type of parking, the vehicles need complex maneuvering and this may cause severe accidents. This arrangement causes obstruction to the road traffic particularly if the road width is less. However, it can accommodate maximum number of vehicles for a given kerb length. Length available for parking N number of vehicles is L = 2.5N.
Off street parking
In many urban centers, some areas are exclusively allotted for parking which will be at some distance away from the main stream of traffic. Such a parking is referred to as off-street. Off-street parking means parking your vehicle anywhere but on the streets. These are usually parking facilities like garages and lots. Off-street parking can be both indoors and outdoors. Off-street parking also includes private lots, garages and driveways. The users of on-street parking are casual users who use the space for a short period of time. Off street parking users differ from short to long-term, i.e. monthly tenants and regular users. In many urban centers, some areas are exclusively allotted for parking which will be at some distance away from the mainstream of traffic. Such a parking is referred to as off-street parking. They may be operated by either public agencies or private firms.

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CHAPTER 1
INTRODUCTION
1.1Background of Study
Time series is a series of measurement over time, usually obtained at the same manner spaced intervals. Meanwhile, time series analysis is a statistical technique that deals with time series data, or trend analysis ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “ISBN” : “0387953515”, “author” : { “dropping-particle” : “”, “family” : “Brockwell”, “given” : “Peter J”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Davis”, “given” : “Richard A”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “0” }, “title” : “Introduction to Time Series and Forecasting , Second Edition Springer Texts in Statistics”, “type” : “book” }, “uris” : “http://www.mendeley.com/documents/?uuid=8e8c016c-bc76-482a-ad69-5e44f9f5324c” } , “mendeley” : { “formattedCitation” : “(Brockwell & Davis, n.d.)”, “manualFormatting” : “(Brockwell & Davis, 2001)”, “plainTextFormattedCitation” : “(Brockwell & Davis, n.d.)”, “previouslyFormattedCitation” : “(Brockwell & Davis, n.d.)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Brockwell and Davis, 2001). Furthermore, time series forecasting is a techniques for the prediction of events through a sequence of time. By analysing the time series, it is used to describe the fundamental structure and the phenomenon as represent by the sequence of observations in the series. Forecasting can be used in variety of studies such as airline industry. Nowadays, airlines has become one of the necessity in people lives, it also helps to improve the national economic and tourism ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Min”, “given” : “Jennifer C H”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Kung”, “given” : “Hsien-hung”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Liu”, “given” : “Hsiang Hsi”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issue” : “10”, “issued” : { “date-parts” : “2010” }, “page” : “2121-2131”, “title” : “Interventions affecting air transport passenger demand in Taiwan”, “type” : “article-journal”, “volume” : “4” }, “uris” : “http://www.mendeley.com/documents/?uuid=aaf21f22-ba94-455e-9b38-3a62b0bfba0c” } , “mendeley” : { “formattedCitation” : “(Min, Kung, & Liu, 2010)”, “plainTextFormattedCitation” : “(Min, Kung, & Liu, 2010)”, “previouslyFormattedCitation” : “(Min, Kung, & Liu, 2010)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Min, Kung and Liu, 2010). Moreover, forecasting can be considered as one of the tool for a better airline management and planning ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Ming”, “given” : “Wei”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Bao”, “given” : “Yukun”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Hu”, “given” : “Zhongyi”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Xiong”, “given” : “Tao”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “2014” }, “title” : “Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models”, “type” : “article-journal”, “volume” : “2014” }, “uris” : “http://www.mendeley.com/documents/?uuid=b61ab13a-c3a0-4a1c-b7ab-d819e4ec24cc” } , “mendeley” : { “formattedCitation” : “(Ming, Bao, Hu, & Xiong, 2014)”, “plainTextFormattedCitation” : “(Ming, Bao, Hu, & Xiong, 2014)”, “previouslyFormattedCitation” : “(Ming, Bao, Hu, & Xiong, 2014)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Ming et al, 2014). According to Aderamo (2010), any airline organisations need to have estimation of expected future demands in order to improve their airline service.
Forecasting has many benefit towards the development of airline and it is depends on number of passenger on that time period ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Andreoni”, “given” : “Alberto”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Postorino”, “given” : “Maria Nadia”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “2006” }, “title” : “A MULTIVARIATE ARIMA MODEL TO FORECAST”, “type” : “article-journal” }, “uris” : “http://www.mendeley.com/documents/?uuid=2f63ef10-8b22-4a0e-b754-89ff03bc6827” } , “mendeley” : { “formattedCitation” : “(Andreoni & Postorino, 2006)”, “plainTextFormattedCitation” : “(Andreoni & Postorino, 2006)”, “previouslyFormattedCitation” : “(Andreoni & Postorino, 2006)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Andreoni & Postorino, 2006). Despite experienced a challenging moments such as political issues, economic issues and many more, the airline industry demand continuously rising. Even though it is keep rising, it has slightly give an impacted towards airline markets. In response to this issue, airlines are constantly improve their service structures in order to eliminate lose and to have continuous profit. Airlines’ passengers may have interest in the demand modelling and simulation, normally when there is a competition among airlines’ market and they need to choose which service they should take ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Andreoni”, “given” : “Alberto”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Postorino”, “given” : “Maria Nadia”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “2006” }, “title” : “A MULTIVARIATE ARIMA MODEL TO FORECAST”, “type” : “article-journal” }, “uris” : “http://www.mendeley.com/documents/?uuid=2f63ef10-8b22-4a0e-b754-89ff03bc6827” } , “mendeley” : { “formattedCitation” : “(Andreoni & Postorino, 2006)”, “plainTextFormattedCitation” : “(Andreoni & Postorino, 2006)”, “previouslyFormattedCitation” : “(Andreoni & Postorino, 2006)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Andreoni & Postorino, 2006).
In Malaysia, the list of airlines’ company are AirAsia, AirAsia X, Firefly, Malindo, Malaysian Airlines and others. The passenger traffic growth in 2017 is expected to overtake growth rates in 2016. In airline companies as well as for all types of companies, demand forecasting is a very significance issue. The success of the managers and companies are much related with suitable strategies which are composed with accurate future forecast. Demand forecasting for available seats in airlines is important to maximize the expected revenue by setting the appropriate fare levels for those seats. The accuracy of the forecast is the most significant tool of the revenue management systems, (MAVCOM).
In this study, we focus on Air Asia airlines as our case study. We would like to forecast the number of Air Asia passengers in Malaysia. In airline industry, it consists of two types of operations namely Full cost carriers (FCC) and low cost carriers (LCC) and Air Asia is one of the low cost carriers type. There are also several LCC such as Air Asia X, Firefly, Berjaya Air, and Sabah Air Aviation (David, 2011). Air Asia is the first low cost carriers company in Malaysia. Moreover, Air Asia also known as the largest and the best low fare in Asia. Air Asia continuously expand with their efficient services, passion toward business and has made a revolution in airline industry. Thus, more people are choosing Air Asia as their choice of airlines.
1.2 Problem Statement
As we know, the airline transport has become a demand nowadays. Based on the previous study (Asrah et al 2018) is a case study about airlines in Malaysia which is AirAsia and Malaysian Airlines. In this study, they compare the distributional behaviour data from the number of Air Asia and Malaysian Airline passenger. As MAS passenger airlines data set are not govern by geometric Brownian motion (GBM), they forecast the number of MAS airline passenger by using Box Jenkins method. Asrah et al, 2013 they forecast the number of Air Asia passenger by using geometric Brownian motion (GBM). As for this research, we use Box Jenkins method to forecast the number of airline passenger of Air Asia. By using forecasting method, it can help in terms of upgrading and improving an airline sector.
1.3 Objective
To study the behaviour of the Air Asia passenger data
To find the best model for Air Asia passenger in Malaysia by using Box Jenkins method
To forecast the number of Air Asia passenger by using the best model
1.4 Scope of Study
In this study, the data have been obtained from Malaysia Airport Holdings Berhad (MAHB). The data obtained were about the total number of passengers that arrived to Kuala Lumpur International Airport (KLIA). These set of time series data are for the number of Air Asia passengers from January 2009 until August 2012.
1.5 Significant of Study
This study has contribute to forecast the number of passenger in Air Asia airline. Besides that, this study also contribute to compare method used to forecast the number of airline passenger which is Air Asia as from previous study they used geometric Brownian motion (GBM) to forecast. This study also helps to enhance better understanding and knowledge about airline industry in Malaysia. This study also will helps the airline industry with the result obtain for them to set appropriate policy for a better management. Moreover, this study also give an overview about the relevant literature in order to explain and develop a better understanding about the airline industry and also the method used. Even though the objective of this study is to forecast the number of Air Asia passenger by using the best model, it is also give a knowledge about other method that has been used by the previous researcher.
CHAPTER 2
LITERATURE REVIEW
2.1 Research in Airlines Industries
Airline Industry has become one of the customer choice as their transport. After a few tragedy happen such as the September 11 terrorist attacks, economic slowdown, several accidents involving airplanes and many mores, this has impacted passenger trust towards airline industry ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Radoslaw R. Okulski”, “given” : “Almas Heshmati”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issue” : “January 2010”, “issued” : { “date-parts” : “2014” }, “title” : “Passengers Transportation Industry Technology Management , Economics and Policy Papers Time Series Analysis of Global Airline”, “type” : “article-journal” }, “uris” : “http://www.mendeley.com/documents/?uuid=c2c180af-de78-4159-8400-8094ff64fdf8” } , “mendeley” : { “formattedCitation” : “(Radoslaw R. Okulski 2014)”, “manualFormatting” : “(Radoslaw, 2014)”, “plainTextFormattedCitation” : “(Radoslaw R. Okulski 2014)”, “previouslyFormattedCitation” : “(Radoslaw R. Okulski 2014)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Radoslaw, 2014). Due to this, there are numerous of study regarding this issue. For example, survey are conducted in order to have an overview a passengers’ needs towards airline industry ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “ISBN” : “9781467315821”, “author” : { “dropping-particle” : “”, “family” : “Asrah”, “given” : “Norhaidah Mohd”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “2012” }, “page” : “479-482”, “title” : “Malaysia Commercial Flight Passengers u201f Safety ( NEWS )”, “type” : “article-journal” }, “uris” : “http://www.mendeley.com/documents/?uuid=7e23f74e-bbee-4c73-91d3-c31ab813f876” } , “mendeley” : { “formattedCitation” : “(Asrah 2012)”, “plainTextFormattedCitation” : “(Asrah 2012)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Asrah 2012). In this research, the survey called People NEWS (Needs, Expectation, Wants and Satisfaction) was conducted to obtain public opinion regarding air travel safety and process in Malaysia. In this paper, they concentrate more on check in and check out steps. Eventually, this survey can be a useful recommendations not only for airlines’ company but also to government for future development of airlines. In a research conducting in Nigeria, they suggest the need for the government to improve the airline system ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Aderamo”, “given” : “Adekunle J”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issue” : “1”, “issued” : { “date-parts” : “2010” }, “page” : “23-31”, “title” : “Demand for Air Transport in Nigeria”, “type” : “article-journal”, “volume” : “1” }, “uris” : “http://www.mendeley.com/documents/?uuid=479ad472-fdf1-44bf-854b-0f2d06bc7802” } , “mendeley” : { “formattedCitation” : “(Aderamo 2010)”, “manualFormatting” : “(Aderamo, 2010)”, “plainTextFormattedCitation” : “(Aderamo 2010)”, “previouslyFormattedCitation” : “(Aderamo 2010)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Aderamo, 2010). For determine future planning, they collect the data on passenger, aircraft and cargo movement to determine the pattern of airlines industry in order to have a future planning.

Besides that, it is also important to improve our understanding towards airlines’ passenger decision making ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/j.jairtraman.2004.06.001”, “ISBN” : “0969-6997”, “ISSN” : “09696997”, “abstract” : “This paper seeks to improving our understanding of air passengers’ decision-making processes by testing a conceptual model that considers service expectation, service perception, service value, passenger satisfaction, airline image, and behavioural intentions simultaneously. For this testing, path analysis via maximum likelihood estimator is applied to data collected from Korean international air passengers. Service value, passenger satisfaction, and airline image are each found to have a direct effect on air passengers’ decision-making processes. u00a9 2004 Elsevier Ltd. All rights reserved.”, “author” : { “dropping-particle” : “”, “family” : “Park”, “given” : “Jin Woo”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Robertson”, “given” : “Rodger”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Wu”, “given” : “Cheng Lung”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Journal of Air Transport Management”, “id” : “ITEM-1”, “issue” : “6”, “issued” : { “date-parts” : “2004” }, “page” : “435-439”, “title” : “The effect of airline service quality on passengers’ behavioural intentions: A Korean case study”, “type” : “article-journal”, “volume” : “10” }, “uris” : “http://www.mendeley.com/documents/?uuid=9ee7cb84-0250-4e2f-ad41-739c7c179873” } , “mendeley” : { “formattedCitation” : “(J. W. Park, Robertson, and Wu 2004)”, “manualFormatting” : “(J. W. Park, Robertson, and Wu ,2004)”, “plainTextFormattedCitation” : “(J. W. Park, Robertson, and Wu 2004)”, “previouslyFormattedCitation” : “(J. W. Park, Robertson, and Wu 2004)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(J. W. Park, Robertson, and Wu ,2004). Service with high quality has become a requirement for gaining customer support and increase the profit. Giving a high quality service has become one of the marketing needs (Ostrowski et al., 1993). It is vital to understand what the passenger need and expect from the service organizations (Jin and Julie, 2000). The effect of airline passengers’ expectations on service perception and passenger satisfaction has to be fully investigated. In Malaysia, ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/j.jairtraman.2005.01.007”, “ISBN” : “0969-6997”, “ISSN” : “09696997”, “abstract” : “Direct competition between full service airlines and no-frills carriers is intensifying across the world. US and European full service airlines have lost a significant proportion of their passengers to low cost carriers, the experience now being repeated in the domestic markets of Asia. This paper attempts to provide answers to a number of critical questions: What are the key drivers of each type of airline’s business model? Is there a difference in passengers’ perceptions between low cost carriers and full service incumbents in a mature European market and in a rapidly developing Asian economy? What are the principle reasons why a passenger chooses a particular airline model? How could a legacy carrier encourage passengers to return and so regain their domestic market share? These questions are addressed using information obtained in passenger surveys that were recently conducted in Europe and Asia. u00a9 2005 Elsevier Ltd. All rights reserved.”, “author” : { “dropping-particle” : “”, “family” : “O’Connell”, “given” : “John F.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Williams”, “given” : “George”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Journal of Air Transport Management”, “id” : “ITEM-1”, “issue” : “4”, “issued” : { “date-parts” : “2005” }, “page” : “259-272”, “title” : “Passengers’ perceptions of low cost airlines and full service carriers: A case study involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines”, “type” : “article-journal”, “volume” : “11” }, “uris” : “http://www.mendeley.com/documents/?uuid=efe14a73-6f34-44e8-99ba-a626d0bde237” } , “mendeley” : { “formattedCitation” : “(Ou2019Connell and Williams 2005)”, “manualFormatting” : “(Ou2019Connell and Williams ,2005)”, “plainTextFormattedCitation” : “(Ou2019Connell and Williams 2005)”, “previouslyFormattedCitation” : “(Ou2019Connell and Williams 2005)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(O’Connell and Williams ,2005) seeks passengers’ perceptions of low cost airlines and full service carriers. Survey have been conducted to determine why passenger are choosing one particular airline over another. The growth of low cost airline industry has become one of the passengers’ choice recently ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/j.jairtraman.2005.01.007”, “ISBN” : “0969-6997”, “ISSN” : “09696997”, “abstract” : “Direct competition between full service airlines and no-frills carriers is intensifying across the world. US and European full service airlines have lost a significant proportion of their passengers to low cost carriers, the experience now being repeated in the domestic markets of Asia. This paper attempts to provide answers to a number of critical questions: What are the key drivers of each type of airline’s business model? Is there a difference in passengers’ perceptions between low cost carriers and full service incumbents in a mature European market and in a rapidly developing Asian economy? What are the principle reasons why a passenger chooses a particular airline model? How could a legacy carrier encourage passengers to return and so regain their domestic market share? These questions are addressed using information obtained in passenger surveys that were recently conducted in Europe and Asia. u00a9 2005 Elsevier Ltd. All rights reserved.”, “author” : { “dropping-particle” : “”, “family” : “O’Connell”, “given” : “John F.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Williams”, “given” : “George”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Journal of Air Transport Management”, “id” : “ITEM-1”, “issue” : “4”, “issued” : { “date-parts” : “2005” }, “page” : “259-272”, “title” : “Passengers’ perceptions of low cost airlines and full service carriers: A case study involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines”, “type” : “article-journal”, “volume” : “11” }, “uris” : “http://www.mendeley.com/documents/?uuid=efe14a73-6f34-44e8-99ba-a626d0bde237” } , “mendeley” : { “formattedCitation” : “(Ou2019Connell and Williams 2005)”, “manualFormatting” : “(Ou2019Connell and Williams, 2005)”, “plainTextFormattedCitation” : “(Ou2019Connell and Williams 2005)”, “previouslyFormattedCitation” : “(Ou2019Connell and Williams 2005)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(O’Connell and Williams, 2005). Graham (2006) identifies the majority of the low-cost airline demand is from leisure travellers while Mason (2005) identifies the number of business traveller that used low cost carrier are increasing and they also view the low cost carrier as good indicator towards business demand. Thus, to maintain consumer interest, airlines need to continue to innovate, providing tourism destinations which meet these requirement.

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2.2 Forecasting method
In order to use forecasting techniques, different situation used different kind of techniques. Firstly, we have to know different forecasting techniques if we want to do a forecasting according to its situation. Some of the techniques are moving average, exponential smoothing (simple, Holt’s method, and winters method), linear regression and Box–Jenkins models. Forecasting have many fields including business and industry, government, economics, environmental sciences, medicine, social science, politics, and finance ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.5465/AMR.1979.4289149”, “ISBN” : “0471823600”, “ISSN” : “0363-7425”, “PMID” : “18491586”, “abstract” : “Comprehensively covering all aspects of long-range forecasting methods relevant to the social, behavioral and management sciences, this book is a synthesis of research in economics, sociology, psychology, transportation, education, and management – with occasional references to work in medicine, meterology, and technology. It describes a variety of forecasting methods, their strengths and weaknesses, and how to use them effectively, shows how to structure a forecasting problem, and gives detailed procedures for evaluating forecasting models in order to select the appropriate method for a particular problem. The book draws upon material from approximately 1300 books and articles, and includes original research by the author.”, “author” : { “dropping-particle” : “”, “family” : “Anderson”, “given” : “C. R.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Academy of Management Review”, “id” : “ITEM-1”, “issue” : “3”, “issued” : { “date-parts” : “1979” }, “page” : “474-475”, “title” : “Long-Range Forecasting: From Crystal Ball to Computer.”, “type” : “article”, “volume” : “4” }, “uris” : “http://www.mendeley.com/documents/?uuid=5887c1eb-1517-4c51-98e9-292c9d2cfc8d” } , “mendeley” : { “formattedCitation” : “(Anderson 1979)”, “plainTextFormattedCitation” : “(Anderson 1979)”, “previouslyFormattedCitation” : “(Anderson 1979)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Anderson 1979). Forecasting are often classified as short-term, medium-term, and long-term ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/j.jairtraman.2005.01.007”, “ISBN” : “0969-6997”, “ISSN” : “09696997”, “abstract” : “Direct competition between full service airlines and no-frills carriers is intensifying across the world. US and European full service airlines have lost a significant proportion of their passengers to low cost carriers, the experience now being repeated in the domestic markets of Asia. This paper attempts to provide answers to a number of critical questions: What are the key drivers of each type of airline’s business model? Is there a difference in passengers’ perceptions between low cost carriers and full service incumbents in a mature European market and in a rapidly developing Asian economy? What are the principle reasons why a passenger chooses a particular airline model? How could a legacy carrier encourage passengers to return and so regain their domestic market share? These questions are addressed using information obtained in passenger surveys that were recently conducted in Europe and Asia. u00a9 2005 Elsevier Ltd. All rights reserved.”, “author” : { “dropping-particle” : “”, “family” : “O’Connell”, “given” : “John F.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Williams”, “given” : “George”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Journal of Air Transport Management”, “id” : “ITEM-1”, “issue” : “4”, “issued” : { “date-parts” : “2005” }, “page” : “259-272”, “title” : “Passengers’ perceptions of low cost airlines and full service carriers: A case study involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines”, “type” : “article-journal”, “volume” : “11” }, “uris” : “http://www.mendeley.com/documents/?uuid=efe14a73-6f34-44e8-99ba-a626d0bde237” } , “mendeley” : { “formattedCitation” : “(Ou2019Connell and Williams 2005)”, “manualFormatting” : “(Ou2019Connell and Williams ,2005)”, “plainTextFormattedCitation” : “(Ou2019Connell and Williams 2005)”, “previouslyFormattedCitation” : “(Ou2019Connell and Williams 2005)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(O’Connell and Williams ,2005). For example, in the research by ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Park”, “given” : “D C”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Marks”, “given” : “R J”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Atlas”, “given” : “L E”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Damborg”, “given” : “M J”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issue” : “2”, “issued” : { “date-parts” : “1991” }, “page” : “442-449”, “title” : “Electric load forecasting using an artificial neural network – Power Systems, IEEE Transactions on”, “type” : “article-journal”, “volume” : “6” }, “uris” : “http://www.mendeley.com/documents/?uuid=fe8978ce-71dd-49a0-9e72-09de31db9a0e” } , “mendeley” : { “formattedCitation” : “(D. C. Park et al. 1991)”, “plainTextFormattedCitation” : “(D. C. Park et al. 1991)”, “previouslyFormattedCitation” : “(D. C. Park et al. 1991)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(D. C. Park et al. 1991), this paper presents an artificial neural network(ANN) approach to electric load forecasting. Neural networks (NNs) have been vigorously promoted in the computer science literature for tackling a wide variety of problems. Recently, statisticians have started to investigate whether NNs are useful for tackling various statistical problems (Cheng and Titterington, 1994) and there has been particular attention to pattern recognition (Bishop, 1995; Ripley, 1996). NNs also appear to have potential application in time series modelling and forecasting but nearly all such work has been published outside the mainstream statistical literature. Besides that, they are also different forecasting techniques that can be used that suited with the field needed. One of the forecasting method was geometric Brownian motion (GBM). This method has a criteria of stationary, normally distributed and independent ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1080/00137910590949904”, “ISBN” : “0013791X”, “ISSN” : “0013791X”, “PMID” : “17552395”, “abstract” : “Abstract The geometric Brownian motion (GBM) process is frequently invoked as a model for such diverse quantities as stock prices, natural resource prices and the growth in demand for products or services. We discuss a process for checking whether a given time series follows the GBM process. Methods to remove seasonal variation from such a time series are also analyzed. Of four industries studied, the historical time series for usage of established services meet the criteria for a GBM; however, the data for growth of emergent services do not.”, “author” : { “dropping-particle” : “”, “family” : “Marathe”, “given” : “Rahul R.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Ryan”, “given” : “Sarah M.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Engineering Economist”, “id” : “ITEM-1”, “issue” : “2”, “issued” : { “date-parts” : “2005” }, “page” : “159-192”, “title” : “On the validity of the geometric Brownian motion assumption”, “type” : “article-journal”, “volume” : “50” }, “uris” : “http://www.mendeley.com/documents/?uuid=2774ff10-c013-4ab1-9aa3-f54ed4007d75” } , “mendeley” : { “formattedCitation” : “(Marathe and Ryan 2005)”, “plainTextFormattedCitation” : “(Marathe and Ryan 2005)”, “previouslyFormattedCitation” : “(Marathe and Ryan 2005)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Marathe and Ryan 2005). In paper by ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1063/1.4823977”, “author” : { “dropping-particle” : “”, “family” : “Mohd”, “given” : “Norhaidah”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Universiti”, “given” : “Asrah”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Hussein”, “given” : “Tun”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Djauhari”, “given” : “Maman”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Bina”, “given” : “Tjahaja”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Indonesia”, “given” : “Statistika”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “View”, “given” : “Plus Highway”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Asrah”, “given” : “Norhaidah Mohd”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issue” : “February”, “issued” : { “date-parts” : “2013” }, “title” : “Time Series Behaviour of the Number of Air Asia Passengers : A Distributional Approach”, “type” : “article-journal” }, “uris” : “http://www.mendeley.com/documents/?uuid=4349d9e3-63b4-46a9-9b42-db03d138022d” } , “mendeley” : { “formattedCitation” : “(Mohd et al. 2013)”, “manualFormatting” : “(Asrah, 2013)”, “plainTextFormattedCitation” : “(Mohd et al. 2013)”, “previouslyFormattedCitation” : “(Mohd et al. 2013)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Asrah, 2013), they has been used GBM method to forecast the number of Air Asia passenger.
2.3 Forecasting Airline Passenger by using Box Jenkins Method
Forecasting airline has been known around the world. For example the research by ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Hong”, “given” : “Wai”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Tsui”, “given” : “Kan”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Ozer”, “given” : “Hatice”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Gilbey”, “given” : “Andrew”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Gow”, “given” : “Hamish”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issue” : “2014”, “issued” : { “date-parts” : “2015” }, “title” : “Forecasting of Hong Kong airport u2019 s passenger throughput”, “type” : “article-journal”, “volume” : “42” }, “uris” : “http://www.mendeley.com/documents/?uuid=da29eb30-65dd-4a5a-a759-e9d5a9b7e492” } , “mendeley” : { “formattedCitation” : “(Hong et al. 2015)”, “plainTextFormattedCitation” : “(Hong et al. 2015)”, “previouslyFormattedCitation” : “(Hong et al. 2015)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Hong et al. 2015), they forecast airport passenger traffic for Hong Kong International Airport and also predict its future growth trend to 2015. They believe that forecasting result obtained can give an overview regarding the developing of HKIA’s future passenger traffic. Thus, in this research they used Box Jenkins ARIMA model for forecasting HKIA’s future passenger throughput. According to prior study, it stated that ARIMA model can accurately forecast airport traffic demands (Abdelghany and Guzhva, 2010). SARIMA model was used to model HKIA’s passenger traffic. SARIMA models predict a steady growth in future airport passenger traf?c, Hong Kong. In addition, scenario analysis suggests that Hong Kong airport’s future passenger traf?c will continue to grow in different magnitudes. In Ming et al (2014), they also applied ARIMA models to forecast air traffic passengers travel. Thus, ARIMA model are suitable to be used as a model to forecast by using airline data.
In a past decade, technological development and the global economic crisis are considered as one of the factors of development that can affect the airline industry. The research by (Rdoslaw and Almas 2010) was conducted to investigate time series analysis of airline industry. They decided to use ARIMA model as it suitable with airline market because it allows us to process regular seasonal fluctuation time series ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1029/WR013i003p00577”, “ISSN” : “19447973”, “author” : { “dropping-particle” : “”, “family” : “McLeod”, “given” : “Angus Ian”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Hipel”, “given” : “Keith William”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Lennox”, “given” : “William C.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Water Resources Research”, “id” : “ITEM-1”, “issue” : “3”, “issued” : { “date-parts” : “1977” }, “page” : “577-586”, “title” : “Advances in Boxu2010Jenkins modeling: 2. Applications”, “type” : “article-journal”, “volume” : “13” }, “uris” : “http://www.mendeley.com/documents/?uuid=4d7f1ec6-83a9-4781-b944-e81bcb0f75be” } , “mendeley” : { “formattedCitation” : “(McLeod, Hipel, and Lennox 1977)”, “plainTextFormattedCitation” : “(McLeod, Hipel, and Lennox 1977)”, “previouslyFormattedCitation” : “(McLeod, Hipel, and Lennox 1977)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(McLeod, Hipel, and Lennox 1977). They use ARIMA (0,1,1)×(0,1,1)12 as the final model for measuring the prediction performance of the model. The purpose of this research basically to analyse the exact future growth of passenger transportation. Thus, in the future it can be expected that an anticipated average of 10,000 more passengers will utilize the world airlines services every month. Furthermore, the research by Asrah et al (2018) they forecast the number of passenger Malaysian Airline (MAS) by using Box Jenkins method. The suitable time series model for data in 2009 is SARIMA (0,0,1)(1,0,0) while the suitable time series model for data in 2012 is SARIMA (2,0,0)(0,1,1). In this study, we use Box Jenkins method to forecast the number of passenger airline of Air Asia.
CHAPTER 3
METHODOLOGY
3.1 Description data
In the airline industry, passenger flows from a source to a destination represent a statistical time series adopted daily, monthly, quarterly, or yearly numbers of air passengers. In this study, the set of data obtain from Malaysia Airport Holdings Berhad (MAHB). The data obtained were about the total number of passengers that arrived to Kuala Lumpur International Airport (KLIA). These set of time series data are for the number of Air Asia passengers from January 2009 until August 2012.
3.2 Normality Test
Normality tests can be conducted in many way such as the Shapiro-Wilk test, the Lilliefors test, the Cramer-von Mises test, the Anderson-Darling test, D’Agostino–Pearson test, the Jarque–Bera test and chi-squared test ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1080/00949655.2010.520163”, “author” : { “dropping-particle” : “”, “family” : “Yap”, “given” : “B W”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Sim”, “given” : “C H”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issue” : “May”, “issued” : { “date-parts” : “2011” }, “title” : “Comparisons of various types of normality tests”, “type” : “article-journal”, “volume” : “9655” }, “uris” : “http://www.mendeley.com/documents/?uuid=0c2fbdde-39df-4e26-bdd1-477e19db5f67” } , “mendeley” : { “formattedCitation” : “(Yap and Sim 2011)”, “plainTextFormattedCitation” : “(Yap and Sim 2011)”, “previouslyFormattedCitation” : “(Yap and Sim 2011)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Yap and Sim 2011). A normality test is used to determine whether the sample data has been drawn from a normally distributed population (within some tolerance). Normal distribution is significance as it is fundamentally assumption of many statistical procedures. Other than that, it is also the most frequents distribution used in statistical theory and application.
3.2.1 Shapiro-Wilk Test
There are nearly 40 test of normality available in the statistical literature .The Shapiro-Wilks is one of the normality designed to detect all departures from the normality. Shapiro and Wilk (1965) test was at first allowed for only sample size of less than 50. It has become the preferred among other test because it has a good power propertiesCITATION Nor11 l 17417 (Mohd Razali , 2011). The hypotheses for Shapiro-Wilk test for normal distribution which are
H0 : The data follow normal distribution
H1: The data do not follow normal distribution
From the hypothesis, the null hypothesis state that the data are normally distributed meanwhile the alternative hypothesis state that the data are not normally distributed. By using Shapiro- Wilk test, the p-value are determined whether the test are significant or not. If the p-value has higher value, the null hypothesis is not rejected, meaning that the data follow normal distribution. The formula for Shapiro-Wilk test is
W=(i=1naiyi)²i=1n(yi-?)² (3.1)
Where ai =constant
3.3 Independent
The independent variable is one of the characteristic of the data where the correlation between the values of the same variable is based on related objects ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “ISBN” : “0387953515”, “author” : { “dropping-particle” : “”, “family” : “Brockwell”, “given” : “Peter J”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Davis”, “given” : “Richard A”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “0” }, “title” : “Introduction to Time Series and Forecasting , Second Edition Springer Texts in Statistics”, “type” : “book” }, “uris” : “http://www.mendeley.com/documents/?uuid=8e8c016c-bc76-482a-ad69-5e44f9f5324c” } , “mendeley” : { “formattedCitation” : “(Brockwell and Davis n.d.)”, “manualFormatting” : “(Brockwell and Davis, 2002)”, “plainTextFormattedCitation” : “(Brockwell and Davis n.d.)”, “previouslyFormattedCitation” : “(Brockwell and Davis n.d.)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Brockwell and Davis, 2002)
3.3.1 Durbin Watson Test
The Durbin Watson Test is a measure of autocorrelation in residual from regression analysis ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/0304-4076(85)90012-0”, “ISBN” : “0709-9231 ;”, “ISSN” : “03044076”, “PMID” : “977649”, “abstract” : “We study two Durbin-Watson type tests for serial correlation of errors inregression models when observations are missing. We derive them by applying standard methods used in time series and linear models to deal with missing observations. The first test may be viewed as a regular Durbin-Watson test in the context of an extended model. We discuss appropriate adjustments that allow one to use all available bounds tables. We show that the test is locally most powerful invariant against the same alternative error distribution as the Durbin-Watson test. The second test is based on a modified Durbin-Watson statistic suggested by King (1981a) and is locally most powerful invariant against a first-order autoregressive process. u00a9 1985.”, “author” : { “dropping-particle” : “”, “family” : “Dufour”, “given” : “Jean Marie”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Dagenais”, “given” : “Marcel G.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Journal of Econometrics”, “id” : “ITEM-1”, “issue” : “3”, “issued” : { “date-parts” : “1985” }, “page” : “371-381”, “title” : “Durbin-Watson tests for serial correlation in regressions with missing observations”, “type” : “article-journal”, “volume” : “27” }, “uris” : “http://www.mendeley.com/documents/?uuid=a5cd9d1e-928d-48be-bb8d-5fda1191725a” } , “mendeley” : { “formattedCitation” : “(Dufour and Dagenais 1985)”, “manualFormatting” : “(Dufour and Dagenais, 1985)”, “plainTextFormattedCitation” : “(Dufour and Dagenais 1985)”, “previouslyFormattedCitation” : “(Dufour and Dagenais 1985)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Dufour and Dagenais, 1985). This test is easy to compute and has optimal power properties against first-order serial dependence (Durbin and Watson, 1950).Autocorrelation is the similarity of a time series over successive time intervals.
DW=t=2T(et-et-1)²t=1Tet² (3.2)
The formula above is for Durbin-Watson where et = yt- ?t and D lies between 0 to 4. The Durbin- Watson statistic interpreted as follow:
If D is close to zero (0), then positive autocorrelation is probably present.

If D is close to two (2), then the model is likely free of autocorrelation.

If D is close to four (4), then negative autocorrelation is probably present.
A rule of thumb is that test statistic values in the range of 1.5 to 2.5 are relatively normal. Values outside of this range could be cause for concern. The test statistic is compared to lower and upper critical values which are DL and DU for specific level of significance ? to test for the autocorrelation. Furthermore, DL are Durbin Watson lower control limit whereas DU are Durbin Watson upper control limit.
The hypothesis testing for positive autocorrelation of the Durbin Watson test are
H0 : ? = 0
H1: ? > 0
This is the criteria for positive correlation
If D < DL rejects H0 If D > DU do not reject H0 If DL <D< DU the test is inconclusive
The hypothesis testing for negative autocorrelation of the Durbin Watson test are
H0 : ? = 0
H1: ? > 0
This is the criteria for negative correlation
If 4-D < DL reject H0 If 4-D > DU do not reject H0 If DL < 4-D < DU the test is inclusive
3.4 Stationary
A time series has a stationarity if a shift in time does not cause a change in the shape of the distribution. Basic properties of the distribution like the mean, variance and covariance are constant over the time. Most forecasting methods assume that a distribution has stationarity. For example, auto-covariance and autocorrelation rely on the assumption of stationarity. It is hard to tell whether the model is stationary or not. Thus, if we are not sure about the stationarity of the model, several testing can be done such as Unit root test ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.5093/cl2010v21n1a6”, “ISSN” : “1130-5274”, “abstract” : “The theme of unit roots in macroeconomic time series have received a great amount of attention in terms of theoretical and applied research over the last three decades. Since the seminal work by Nelson and Plosser (1982), testing for the presence of a unit root in the time series data has become a topic of great concern. This issue gained further momentum with Perron’s 1989 paper which emphasized the importance of structural breaks when testing for unit root processes. This paper reviews the available literature on unit root tests taking into account possible structural breaks. An important distinction between testing for breaks when the break date is known or exogenous and when the break date is endogenously determined is explained. We also describe tests for both single and multiple breaks. Additionally, the paper provides a survey of the empirical studies and an application in order for readers to be able to grasp the underlying problems that time series with structural breaks are currently facing.”, “author” : { “dropping-particle” : “”, “family” : “Glynn”, “given” : “John”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Perera”, “given” : “Nelson”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Verma”, “given” : “Reetu”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Journal of Quantitative Methods for Economics and Business Administration”, “id” : “ITEM-1”, “issue” : “1”, “issued” : { “date-parts” : “2007” }, “page” : “63-79”, “title” : “Unit Root Tests and Structural Breaks: A Survey with Applications”, “type” : “article-journal”, “volume” : “3” }, “uris” : “http://www.mendeley.com/documents/?uuid=5ede21ae-6a9d-4a2b-8cf4-d297fd8071f3” } , “mendeley” : { “formattedCitation” : “(Glynn, Perera, and Verma 2007)”, “plainTextFormattedCitation” : “(Glynn, Perera, and Verma 2007)”, “previouslyFormattedCitation” : “(Glynn, Perera, and Verma 2007)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Glynn, Perera, and Verma 2007), KPSS test, a run sequence plot, The Priestley-Subba Rao (PSR) Test or Wavelet-Based Test.

Figure 4.1: The plot on the left is a stationary with no obvious trend while the plot on the right shows seasonality and is non stationary ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “ISBN” : “0387953515”, “author” : { “dropping-particle” : “”, “family” : “Brockwell”, “given” : “Peter J”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Davis”, “given” : “Richard A”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “0” }, “title” : “Introduction to Time Series and Forecasting , Second Edition Springer Texts in Statistics”, “type” : “book” }, “uris” : “http://www.mendeley.com/documents/?uuid=8e8c016c-bc76-482a-ad69-5e44f9f5324c” } , “mendeley” : { “formattedCitation” : “(Brockwell and Davis n.d.)”, “manualFormatting” : “(Brockwell and Davis, 2001)”, “plainTextFormattedCitation” : “(Brockwell and Davis n.d.)”, “previouslyFormattedCitation” : “(Brockwell and Davis n.d.)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Brockwell and Davis, 2001).

3.4.1 The Augmented Dickey-Fuller (ADF) test
The Augmented Dickey Fuller Test (ADF) is a unit root test for stationarity ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Ac”, “given” : “The”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “0” }, “title” : “Checking for stationarity”, “type” : “article-journal” }, “uris” : “http://www.mendeley.com/documents/?uuid=89f9b333-bf90-401a-872b-0c06bd824e67” } , “mendeley” : { “formattedCitation” : “(Ac n.d.)”, “manualFormatting” : “(Ac , 2010)”, “plainTextFormattedCitation” : “(Ac n.d.)”, “previouslyFormattedCitation” : “(Ac n.d.)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Ac , 2010). Unit roots can cause unpredictable results in a time series analysis. This test can be used with serial correlation. The hypothesis testing is followed by
H0:?=0 (There is a unit root and the series is nonstationary)
H1:?<0 (There is no unit root and the series is stationary)
The decision is to reject the null hypothesis if the t-statistics is greater than the critical value from the Dickey-Fuller table. Therefore, it conclude that the data is not stationary data.

3.5 Box Jenkins Method
The Box-Jenkins method is a time series analysis, forecasting and can be used in many areas and situation which involve in choosing a suitable model. The Box-Jenkins method is one of the most popular time series forecasting methods in business and economics. The method uses a systematic procedure to select an appropriate model, namely, Integrated Autoregressive Moving Average (ARIMA) models. Following Johnson (1997), the general notation for the order of a seasonal ARIMA model with both seasonal and non-seasonal factors is ARIMA(p,d,q)×(P,D,Q), and the term (p,d,q) gives the order of the non-seasonal part of the ARIMA model, the term (P,D,Q) gives the order of the seasonal part. A general ARIMA model has the following form (Bowerman and O’Connell, 1993 ).

?p(B)?p(BL)(1-BL)D(1-B)dyt=a+?q(B)?Q(BL)?t (3.3)
where
? (B) = autoregressive operators
?(B) = moving average operators
B = back shift operator
?t = random error with normal distribution N (0, ?2 );
a = constant,
yt = time series data, transformed if necessary.
Figure 4.2 shows the flowchart of Box Jenkins method.

Figure 4.2 The flowchart of Box Jenkins method ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Madsen”, “given” : “Henrik”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “0” }, “title” : “Time Series Analysis”, “type” : “article-journal” }, “uris” : “http://www.mendeley.com/documents/?uuid=ed26418a-a8b9-4b88-aa20-9a22666368e4” } , “mendeley” : { “formattedCitation” : “(Madsen n.d.)”, “manualFormatting” : “(Madsen, 2015)”, “plainTextFormattedCitation” : “(Madsen n.d.)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Madsen, 2015) .

By using the following process, the Box-Jenkins method can be carried out. The first step in Box Jenkins is Model identification. By using Historical data, it can be used to identify appropriate Box Jenkins model. Firstly, time series plotting can be used to check whether there is a liner trend, stationarity, outliers, seasonal pattern and others in the time series, as well as the mean of the time series is constant or not. Then, the natural logarithmic transformation is applied to stabilise the variance if the mean of the time series is not relatively constant over time.

In order to decide whether the variable need to transform or not, can be determine by using time series plot. The Box-Cox transformation is one of the method to transform the data into normality ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1093/hcr/28.4.612”, “ISBN” : “1468-2958”, “ISSN” : “03603989”, “PMID” : “21270539”, “abstract” : “BACKGROUND: Many different sexual isolation and sexual selection statistics have been proposed in the past. However, there is no available software that implements all these statistical estimators and their corresponding tests for the study of mating behaviour.\n\nRESULTS: JMATING is an easy-to-use program developed in Java for the analysis of mating frequency data to study sexual selection and sexual isolation effects from laboratory experiments as well as descriptive studies accomplished in the wild. The software allows the re-organization of the data previous to the analysis, the estimation of the most important estimators, and a battery of complementary statistical tests.\n\nCONCLUSION: JMATING is the first complete and versatile software for the analyses of mating frequency data. It is available at http://www.uvigo.es/webs/c03/webc03/XENETICA/XB2/JMsoft.htm and requires the Java runtime environment.”, “author” : { “dropping-particle” : “”, “family” : “Carvajal-Rodriguez”, “given” : “Antonio”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Rolan-Alvarez”, “given” : “Emilio”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Bailey”, “given” : “Robert C”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Bailey”, “given” : “Robert C”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Byrnes”, “given” : “Janice”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Byrnes”, “given” : “Janice”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Gilbert”, “given” : “D. G. DG”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Starmer”, “given” : “W.T. T.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Sakia”, “given” : “R M”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Brown”, “given” : “James Dean”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Jin Xiong”, “given” : “”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Levine”, “given” : “Timothy R.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Hullett”, “given” : “Craig R.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Rolan-Alvarez”, “given” : “Emilio”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Caballero”, “given” : “Armando”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Pu00e9rez-Figueroa”, “given” : “A”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Una-Alvarez”, “given” : “Jacobo”, “non-dropping-particle” : “de”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Conde-Padin”, “given” : “Paula”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Rolan-Alvarez”, “given” : “Emilio”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Ranganathan”, “given” : “Yuvaraj”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Borges”, “given” : “Renee M”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Valls”, “given” : “Ricardo a”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Sc”, “given” : “M”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Evolution”, “id” : “ITEM-1”, “issue” : “1”, “issued” : { “date-parts” : “2008” }, “page” : “30-36”, “title” : “Why and How Should Geologists Use Compositional data Analysis”, “type” : “article-journal”, “volume” : “6” }, “uris” : “http://www.mendeley.com/documents/?uuid=de68c91f-1ec6-45cb-9744-acf478e22f8b” } , “mendeley” : { “formattedCitation” : “(Carvajal-Rodriguez et al. 2008)”, “plainTextFormattedCitation” : “(Carvajal-Rodriguez et al. 2008)”, “previouslyFormattedCitation” : “(Carvajal-Rodriguez et al. 2008)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }(Carvajal-Rodriguez et al. 2008). Power family of transformation are used to deal with nonconstant variance given by
y(?)= y?-1 ? , if ??0; logy , if ?=0. y(?)= (y+?2)?1-1? , if ?1?0; log(y+?2 ), if ?1=0. (3.4)
According to Montgomery et al (2015), if ?= 1, there is no transformation. The value of ? used with time series data is ?= -2(reciprocal square transformation), ? = -1(reciprocal transformation), ?=-0.5(reciprocal root transformation), ?=0(logarithm transformation), ?=0.5(square root transformation) and ?;1(square transformation). The table below shows the summary of transformation based on the value of ?.

Table 3.1 summary of transformation based on the value of ?
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Power Transformation
1 “raw”
-2 Reciprocal square
-1 Reciprocal
-0.5 Reciprocal root
0 Logarithm
0.5 Square root
;1 Square
After that, if the mean is still not stationary, differencing can be applied to transform it into stationary if it is not stationary. Differencing is one of the way that can make time series stationary. It also help to stabilize the mean of a time series by removing changes in the level of a time series, and so eliminating or reducing trend and seasonality.The first differencing operater, defined by
yt’=yt -yt-1=1-Byt (3.5)
where
B= backward shift operator
Sometimes, the differenced data are not appear stationary so it is need to difference the data second time in order to obtain stationary series. The second differencing is
= yt’ – yt-1′ =(yt-yt-1)-(yt-1-yt-2)
=yt-2yt-1+yt-2 =(1-B)²yt =(1-2B+B²) (3.6)
When the time series has seasonal component, a seasonal differencing can be used. A seasonal difference is the difference between an observation and the corresponding observation from the previous year. So,
yt’= yt-yt-m (3.7)
where m=number of seasons.

These are also called “lag-m differences” as we subtract the observation after a lag of m periods.
When the time series is in a stationary condition, the model order of autoregressive (AR) compnent and moving average (MA) component can be determined by using graphical plot of autocorrelation function (ACF) and partial autocorrelation function (PACF) . Autoregression model used a linear combination of past values of the variable. The autoregressive model of order p can be written as
yt=c+?1+yt-1+?2yt-2+…+?pyt-p+et (3.8)
where et = white noise
?= coefficient
It is AR(p) model. Rather than use past values of the forecast variable in a regression, a moving average model uses past forecast errors in a regression-like model. Then, the moving average model of order q can be written as
yt= c+et+?1et-1+?2et-2+…+?qet-q (3.9)
where et is a white noise. This is a MA (q) model.

The value of p can be determined from the partial autocorrelations function (PACF). If the ACF exponential decay and PACF cuts off, the model suggested the AR term. The value of q can be determined by autocorrelation function (ACF). If the ACF cut offs and PACF exponential decay, the model suggested the MA term. If the ACF and PACF shows the exponential delay, then the model is ARMA process. The behaviour of ACF and PACF for stationary are summarized in Table 3.2
Table 3.2 The summary behaviour of ACF and PACF for stationary
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Model ACF PACF
MA(q): moving average of order q Cut off after lag q Dies down
AR(p): autoregressive of order p Dies down Cuts off after lag p
ARMA(p,q):mixed autoregressive moving average of order (p , q) Dies down Dies down
AR(p) or MA(q) Cuts off after lag q Cuts off after lag p
No order AR or MA (White Noise or Random process) No spike No spike
The full model can be written as
yt’= c+ ?1 +…+ yt-1′ ?p + …+ yt-p’+?1et-1+…+?qet-q+et (3.10)
where
yt’ = differenced series
We can call this an ARIMA (p,d,q) model, where
p = order of the autoregressive part
d = degree of first differencing involved
q = order of moving average part
Next, the general seasonal ARIMA model of orders (p,d,q)x(P,D,Q) with period d is
(1-?pB)(1-?pBm) (1-B)(1-Bd)yt = (1-?qB)(1+?Q Bm)et (3.11)
where
m = number of observations per year
?pB = seasonal autoregressive operator for non-seasonal part of model
?pB= seasonal autoregressive operator for seasonal part of model
?qB = seasonal moving average operator for non-seasonal part of model
?QB= seasonal moving average operator for seasonal part of model
During a second stage which is estimation stage, estimate the model coefficients by selecting the best-fit model based on the smallest values of AIC and SIC tests. Furthermore, in order to check the adequacy of the estimated model diagnostic checking is carried out and if need to, alternative models may be considered. By using ACF and PACF residuals, it can verify the “white noise” characteristics of the residual series from the selected model when the ACF and PACF residuals is within the 0.05 significance level.
Next, Ljung–Box Chi-Square statistic can be used as a diagnostic tool to test the lack of fit of a time series model (Ljung ; Box, 1978). Ljung–Box Chi-Square statistic is one of the way to assess if the residual from the Box Jenkins model follow the assumptions. Hypothesis testing for the Ljung–Box Chi-Square statistic is:
H0 : The model is adequate
H1 : The model is inadequate
If the p-value of the Ljung-Box Chi Square statistic is small (say, p-value;0.05), the null hypothesis are rejected thus the selected model is considered inadequate, and then a modified model will be established until a satisfactory model can be determined.

Next, forecast can be calculated. The main purpose of fitting ARMA schemes is to project the series forward beyond the sample period or out of sample. It should be noted that, in all that follows we will assume that observations are only available for periods 1 to n, and that all forecasts are made conditional on information available at time n. We look at the residuals to determine how accurate the model predicts. The desired accuracy of the forecasts depends on the analyst’s goal.

3.6 Mean Absolute Percent Error (MAPE)
The MAPE (Mean Absolute Percent Error) measures the size of the error in percentage terms. It is calculated as the average of the unsigned percentage error. Percentage errors have the advantage of being scale-independent, and so are frequently used to compare forecast performance between different data sets.
MAPE= (1n|Actual-Forecast||Actual|)*100 (3.12)
where
n= number of predicted values
The smaller the value of MAPE, the more accurate the forecast. The judgement of forecast accuracy based on MAPE value was summarized in the Table 3.3 below
Table 3.3 The judgement of forecast accuracy based on MAPE value
MAPE Judgement of forecast accuracy
Less than 10% Highly accurate
11% to 20% Good forecast
21% to 50% Reasonable forecast
More than 51% Inaccurate forecast
3.7 Mean Square Error (MSE)
The Mean Square Error (MSE) is a widely used criterion for the choice of a forecasting performance rule. The minimum the value of MSE, the more accurate the forecast. Mean Square Error (MSE) is a measure of dispersion of forecast errors by taken the average of the squared individual errors.
Formula:
MSE = (actual-forecast)²n (3.13)
n= number of predicted values
CHAPTER 4
EXPECTED RESULT
4.1 Expected result
In this study, our objective is to study the behaviour of the Air Asia passenger data by identify whether there is a trend or pattern by using time series plot. By using the result obtained from the time series plot, we can determine whether the data have trend, seasonality or cyclic behavior that can be seen clearly from the output.
Moreover, we need to apply ARIMA model in Air Asia passenger data from January 2008 until Ogos 2012 in order to find the best forecasting model. We are expecting the best model will be determined which is seasonal ARIMA model.
Furthermore, we need to forecast the future of Air Asia passenger for January 2008 until Ogos 2012 by using Box Jenkins Method so that the researchers can obtain a wide knowledge regarding Air Asia passenger data besides other literature review on Air Asia passenger that we can make it as a reference. Based on the result, the trend of the Air Asia passenger can be identified in the future.
Last but not least, we have to evaluate the forecasting performance of ARIMA model. The best forecasting model will be determined and chosen from the forecasting accuracy measure which is MAPE and MSE. The forecasting model with the most minimum forecasting error will be selected as the best forecasting model.
David, J. R. (2011). Budget airlines. Retrieved November 11, 2011, from http://blog.malaysia-asia.my/2011/08/budget-airlines.html
ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY Ac, The. “Checking for Stationarity.”
Carvajal-Rodriguez, Antonio et al. 2008. “Why and How Should Geologists Use Compositional Data Analysis.” Evolution 6(1): 30–36. http://en.wikibooks.org/wiki/Why,_and_How,_Should_Geologists_Use_Compositional_Data_Analysis%5Cnhttp://webs.uvigo.es/rolan/articles/emi55.pdf%5Cnhttp://www.jstor.org/stable/10.2307/2408793%5Cnhttp://www.jstor.org/stable/2408793%5Cnhttp://www.biomedcentral.

Glynn, John, Nelson Perera, and Reetu Verma. 2007. “Unit Root Tests and Structural Breaks: A Survey with Applications.” Journal of Quantitative Methods for Economics and Business Administration 3(1): 63–79.

Yap, B W, and C H Sim. 2011. “Comparisons of Various Types of Normality Tests.” 9655(May).

ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY Aderamo, Adekunle J. 2010. “Demand for Air Transport in Nigeria.” 1(1): 23–31.

Anderson, C. R. 1979. “Long-Range Forecasting: From Crystal Ball to Computer.” Academy of Management Review 4(3): 474–75.

Hong, Wai et al. 2015. “Forecasting of Hong Kong Airport ‘ s Passenger Throughput.” 42(2014).

Marathe, Rahul R., and Sarah M. Ryan. 2005. “On the Validity of the Geometric Brownian Motion Assumption.” Engineering Economist 50(2): 159–92.

McLeod, Angus Ian, Keith William Hipel, and William C. Lennox. 1977. “Advances in Box?Jenkins Modeling: 2. Applications.” Water Resources Research 13(3): 577–86.

Ming, Wei, Yukun Bao, Zhongyi Hu, and Tao Xiong. 2014. “Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models.” 2014.

Mohd, Norhaidah et al. 2013. “Time Series Behaviour of the Number of Air Asia Passengers?: A Distributional Approach.” (February).

O’Connell, John F., and George Williams. 2005. “Passengers’ Perceptions of Low Cost Airlines and Full Service Carriers: A Case Study Involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines.” Journal of Air Transport Management 11(4): 259–72.

Park, D C, R J Marks, L E Atlas, and M J Damborg. 1991. “Electric Load Forecasting Using an Artificial Neural Network – Power Systems, IEEE Transactions On.” 6(2): 442–49.

Park, Jin Woo, Rodger Robertson, and Cheng Lung Wu. 2004. “The Effect of Airline Service Quality on Passengers’ Behavioural Intentions: A Korean Case Study.” Journal of Air Transport Management 10(6): 435–39.

Radoslaw R. Okulski, Almas Heshmati. 2014. “Passengers Transportation Industry Technology Management , Economics and Policy Papers Time Series Analysis of Global Airline.” (January 2010).

ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY Aderamo, A. J. (2010). Demand for Air Transport in Nigeria, 1(1), 23–31.

Andreoni, A., & Postorino, M. N. (2006). A MULTIVARIATE ARIMA MODEL TO FORECAST.

Brockwell, P. J., & Davis, R. A. (n.d.). Introduction to Time Series and Forecasting , Second Edition Springer Texts in Statistics.

Min, J. C. H., Kung, H., & Liu, H. H. (2010). Interventions affecting air transport passenger demand in Taiwan, 4(10), 2121–2131.

Ming, W., Bao, Y., Hu, Z., & Xiong, T. (2014). Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models, 2014.

Mohd, N., Universiti, A., Hussein, T., Djauhari, M., Bina, T., Indonesia, S., … Asrah, N. M. (2013). Time Series Behaviour of the Number of Air Asia Passengers?: A Distributional Approach, (February). https://doi.org/10.1063/1.4823977
http://www.mavcom.my/en/2017/08/15/malaysia-boasts-third-largest-aviation-passenger-market-asean/ BIBLIOGRAPHY Aderamo, A. J. (2010). Demand for Air Transport in Nigeria. 1-9.

Alberto Andreoni, M. N. (2006). A MULTIVARIATE ARIMA MODEL TO FORECAST AIR TRANSPORT DEMAND . 1-14.

Okulski, R. R. (2010). Time Series Analysis of Global Airline. 1-52.

Shabri, A. b. (n.d.). COMPARISION OF TIME SERIES FORECASTING METHODS USING NEURAL NETWORKS AND BOX-JENKINS MODEL . 1-6.

Taiwan, I. a. (2010). Jennifer C. H. Min1*, Hsien-Hung Kung2,3 and Hsiang Hsi Liu4 . FULL LENGTH RESEARCH PAPER, 1-11.

WeiMing, Y. (2014). Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models. Research Article, 2014, 1-15. doi:http://dx.doi.org/10.1155/2014/567246

BIBLIOGRAPHY Abdelghany, A., & Guzhva, V. ( 2010). A time-series modelling approach for airport short-term demand forecasting. Research Article.

Aderamo, A. J. (2010). Demand for Air Transport in Nigeria. Economic Journal, 1-9.

Norhaidah Mohd Asrah, M. A. (2013). Time series behaviour of the number of Air Asia passengers: A distributional approach. Conference paper, 1-7.

Norhaidah Mohd Asrah, M. A. (n.d.). Malaysia Commercial Flight Passengers? Safety (NEWS) . 1-4.

Wai Hong Kan Tsui a, *. H. (2013). Forecasting of Hong Kong airport’s passenger throughput. 1-16.

Gardner, E. S., & McKenzie, E. (1988). Model identification in exponential smoothing. Journal of the Operational Research Society, 39(9), 863–867. http://doi.org/10.2307/2583529
APPENDIX
GANTT CHART
TITLE: TIME SERIES ANALYSIS OF AIRLINE PASSENGERS IN MALAYSIA BY USING BOX JENKINS
WEEKS W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14
1. Resource collection (Articles, Journal and books) 2. Introduction 3. Literature Review 4. Methodology 5. Expected Results 6. Submission of BDP proposal to supervisor 7. Submission of BDP proposal approved by supervisor to examiners 8. preparation for oral presentation 9. Oral Presentation 10. Final proposal correction (after oral presentation) 11. Submission of corrected project proposal Expected Actual

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