2.1 or not. The key element for

 

 

 

2.1 Intrusion
detection system: An intrusion detection system is security software which
is used for automatic alert if anyone tries to enter in system through
malicious activities or through security policy. It examining vulnerabilities
in the system by monitoring system activity, and analysis of patterns based on
already known attacks. It also monitors the internet automatically for latest
threats which could attack in future.

2.2 Basic
components of IDS: It is hardware or software system. It
continuous monitors the activity which is happening in system or network and analyzing
them for malicious activities. It consists of three subsystems or
components: 

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2.2.1 Data Pre-processor: It collects
and provides audit data in a special format to the next division (analyzer) for
making decision and it referred as audit log. 

2.2.2 Analyzer (Intrusion Detector): It analyze
audit log which is came from Data pre processor .Multiple intrusion detection
techniques can be used which is pattern matching, machine learning, data mining
and statistical techniques . The strength of overall system is detected by
analyzer. 

2.2.3 Response Engine: It says
how to response on attack when it is detected and it control the reaction
mechanism. A security policy is determined by predefined attack.

 

2.3 INTRUSION
DETECTION TECHNIQUES

2.3.1 Signature Detection (SD): It
deal with set of rules or signatures or predefined knowledge which decide that
pattern is intruder or not. As a result, this system shows high levels of
accuracy and minimal number of false positives. It is great technique which is
for   known attack detection but fails
for unknown attacks detection. It is easy in maintaining and updating
preconfigured rules.

2.3.2 Anomaly Detection (AD):
Anomaly (or behavioural) detection technique is concerned with identifying
events that appear to be anomalous with respect to normal system behaviour. It
involves the collection of data relating to the behaviour of legitimate users
over a period of time, and then applies statistical tests to the observed
behaviour, which determines whether that behaviour is legitimate or not. The
key element for using this approach efficiently is to generate rules in such a
way that it can lower the false alarm rate for unknown as well as known
attacks.

2.3.3. Soft Computing based Detection: In
intrusion detection system we use the uncertain and partially data which is the
concept of soft. Various techniques in soft computing which is used for
improving accuracy for detection and efficiency of signature or anomaly
detection based IDS they are  Artificial
Neural Network (ANN), Fuzzy logic, Association rule mining etc . ANNs are used for
generalize data in intrusion detection system. The types of ANN used in IDS are
as follows:

·        
Multi-Layer Feed- Forward (MLFF) neural nets

·        
Multi-Layer Perceptron (MLP)

·        
Back Propagation (BP). 

 

 

 

2.1 Intrusion
detection system: An intrusion detection system is security software which
is used for automatic alert if anyone tries to enter in system through
malicious activities or through security policy. It examining vulnerabilities
in the system by monitoring system activity, and analysis of patterns based on
already known attacks. It also monitors the internet automatically for latest
threats which could attack in future.

2.2 Basic
components of IDS: It is hardware or software system. It
continuous monitors the activity which is happening in system or network and analyzing
them for malicious activities. It consists of three subsystems or
components: 

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For You For Only $13.90/page!


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2.2.1 Data Pre-processor: It collects
and provides audit data in a special format to the next division (analyzer) for
making decision and it referred as audit log. 

2.2.2 Analyzer (Intrusion Detector): It analyze
audit log which is came from Data pre processor .Multiple intrusion detection
techniques can be used which is pattern matching, machine learning, data mining
and statistical techniques . The strength of overall system is detected by
analyzer. 

2.2.3 Response Engine: It says
how to response on attack when it is detected and it control the reaction
mechanism. A security policy is determined by predefined attack.

 

2.3 INTRUSION
DETECTION TECHNIQUES

2.3.1 Signature Detection (SD): It
deal with set of rules or signatures or predefined knowledge which decide that
pattern is intruder or not. As a result, this system shows high levels of
accuracy and minimal number of false positives. It is great technique which is
for   known attack detection but fails
for unknown attacks detection. It is easy in maintaining and updating
preconfigured rules.

2.3.2 Anomaly Detection (AD):
Anomaly (or behavioural) detection technique is concerned with identifying
events that appear to be anomalous with respect to normal system behaviour. It
involves the collection of data relating to the behaviour of legitimate users
over a period of time, and then applies statistical tests to the observed
behaviour, which determines whether that behaviour is legitimate or not. The
key element for using this approach efficiently is to generate rules in such a
way that it can lower the false alarm rate for unknown as well as known
attacks.

2.3.3. Soft Computing based Detection: In
intrusion detection system we use the uncertain and partially data which is the
concept of soft. Various techniques in soft computing which is used for
improving accuracy for detection and efficiency of signature or anomaly
detection based IDS they are  Artificial
Neural Network (ANN), Fuzzy logic, Association rule mining etc . ANNs are used for
generalize data in intrusion detection system. The types of ANN used in IDS are
as follows:

·        
Multi-Layer Feed- Forward (MLFF) neural nets

·        
Multi-Layer Perceptron (MLP)

·        
Back Propagation (BP). 

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