alexa Intrusion Detection in Manet
ISSN ONLINE(2278-8875) PRINT (2320-3765)

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering
Open Access

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Research Article

Intrusion Detection in Manet

Prof. Mrs. Poonam Gupta1, Mrs. Mugdha Kirkire2
  1. Head of the Dept, Dept. of Computer Engineering, GHRCEM, Pune, India
  2. Lecturer, Dept. of Computer Engineering, GHRCEM, Pune, India
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Abstract

Now days mobile ad hoc networks (MANETs) have become a very important research topic as no of mobile users are incremented day by day. MANET will provide communications in the network without any of a fixed infrastructure so it can be used for many applications such as rescue operations, tactical operations, environmental monitoring, conferences, also. But the flexibility in such environment have the challenged of risk of security. Our system supports mainly two parts. 1. To detect intruder attacks in mobile ad hoc network (MANET). 2. To detect the type of attack in mobile ad hoc network (MANET) and apply local intrusion detection or network intrusion detection for the attack. It will work for both anomaly detection and misuse detection mechanism. By the Observation of the attack signatures, we find that there are some attack signatures dependent on other previous attack signatures. This is due to the new attack is a derivative from the previous attack. To apply the intrusion detection technique this paper introduces a priory known approach known as acknowledgement based approach which is used to detect intrusion in mobile ad hoc network (MANET) and uses intrusion detection technique like matching algorithm. It approaches a technique of developing a network safety by describing network behaviour structure that point out offensive use of the network and also look for the occurrence of those patterns while such an approach may be accomplished of detecting different types of known intrusive actions, it would allow new or undocumented types of attacks to go invisible. As a result, this leads to a system which monitors and learns normal network behavior and then detects deviations from the normal network behaviour.

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