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.
This work was supported by grant No. R01-2004-000-10618-0 from the Basic Research Program of the Korea Science and Engineering Foundation.
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Lim, D., Chung, J., Ahn, S. (2006). Using Genetic Algorithm for Network Status Learning and Worm Virus Detection Scheme. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_54
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DOI: https://doi.org/10.1007/11875581_54
Publisher Name: Springer, Berlin, Heidelberg
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