An Efficient Modular Approach of Intrusion Detection System based on MSPSO-DT

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Mr.Mangesh R Umak
Dr. Rachana Mishra


Intrusion detection is the act of detecting unwanted traffic on a network or a device. An IDS can be a piece of installed software or a physical appliance that monitors network traffic in order to detect unwanted activity and events such as illegal and malicious traffic, traffic that violates security policy, and traffic that violates acceptable use policies. However, Intrusion detection systems face a number of challenges. One of the important challenges is that, the input data to be classified is in a high dimension feature space. In this paper, we are trying to present MSPSO-DT intrusion detection system. Where, Multi Swam Particle Swarm Optimization (MSPSO) is used as a feature selection algorithm to maximize the C4.5 Decision Tree classifier detection accuracy and minimize the timing speed. To evaluate the performance of the presented MSPSO-DT IDS we are trying to use several experiments on NSL-KDD benchmarked network intrusion detection dataset. Based on the MSPSO-DT system is improved intrusion detection accuracy more than 99.43%, speedup testing time is 11.13 sec and detection speed increased as compare to existing system. Moreover if the intrusion detection accuracy is improved, then we defiantly reduce network traffic problem.

Keywords: Network Security; Intrusion Detection System; Feature Selection; Multi Swam Particle Swarm Optimization; Decision Tree.


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