Abstract
The update of rules is the key to success for rule-based network intrusion detection system because of the endless appearance of new attacks. To efficiently extract classification rules from the vast network traffic data, this paper gives a new approach based on Particle Swarm Optimization (PSO) and introduces a new coding scheme called ”indexical coding” in accord with the feature of the network traffic data. PSO is a novel optimization technique and has been shown high performance in numeric problems, but few researches have been reported in rule learning for IDS that requires a high level representation of the individual, this paper makes a study and demonstrates the performance on the 1999 KDD cup data. The results show the feasibility and effectiveness of it.
Supported by the National Natural Science Foundation of China under Grant No. 60673161, the Key Project of Chinese Ministry of Education under Grant No.206073, the Project supported by the Natural Science Foundation of Fujian Province of China(No.2006J0027).
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Guolong, C., Qingliang, C., Wenzhong, G. (2007). A PSO-Based Approach to Rule Learning in Network Intrusion Detection. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_72
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DOI: https://doi.org/10.1007/978-3-540-71441-5_72
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