The application of genetic algorithm to intrusion detection in MP2P network

Authors:
Lu Li;Guoyin Zhang;Jinyuan Nie;Yingjiao Niu;Aihong Yao
Affiliations:
Harbin Engineering University, Harbin, China;Harbin Engineering University, Harbin, China;Defence Industry Secrecy Examination and Certification Center, China;Harbin Engineering University, Harbin, China;Harbin Engineering University, Harbin, China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2012

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Abstract

With the rapid development of MP2P network, high network security is required. However, the existing intrusion detection scheme for network security does not perform well enough. Aiming at the characteristics of MP2P, this paper proposes an intrusion detection method based on the genetic algorithm, which selects the initial population from the known attack data, extracts data attack characteristics by reproducing, crossover, and mutating themselves, and thus changes from passive defense to active detection. Research results verify that, the application of genetic algorithm could enhance intrusion detection in terms of the dynamic monitoring of internal and external network attacks, and thus gains real-time protection for MP2P networks.