Using genetic algorithm for network status learning and worm virus detection scheme

Authors:
Donghyun Lim;Jinwook Chung;Seongjin Ahn
Affiliations:
Dept. of Computer Engineering, Sungkyunkwan Univ., Suwon, South Korea;Dept. of Computer Engineering, Sungkyunkwan Univ., Suwon, South Korea;Dept. of Computer Education, Sungkyunkwan Univ., Seoul, South Korea
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

This paper tries to propose the worm virus detection system that focuses on many connection attempts, more frequently occurring in the process of scanning than their common transmission processes. And this paper tries to determine the critical value of connection attempt by using the ordinary time network traffic learning technique which applies the genetic algorithm in order to ensure accurate detection of virus, depending on the status of network. This system can reduce the damage from worm virus more quickly than the pattern-founded worm virus detection system because it applies the common characteristics of worm viruses to detect them, and the criteria for judgment can be altered in its application though the network may change.