Intrusion detection based on immune clonal selection algorithms

Authors:
Liu Fang;Qu Bo;Chen Rongsheng
Affiliations:
School of Computer Science and Engineering, Xidian University, Xi'an, China;School of Computer Science and Engineering, Xidian University, Xi'an, China;School of Computer Science and Engineering, Xidian University, Xi'an, China
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

Citing 2
Cited 2

Architecture for an Artificial Immune System

Evolutionary Computation
Learning concept classification rules using genetic algorithms

IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2

Review: The use of computational intelligence in intrusion detection systems: A review

Applied Soft Computing
Intrusion detection based on clustering organizational co-evolutionary classification

FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

Immune clone selection algorithm is a new intelligent algorithm which can effectively overcome the prematurity and slow convergence speed of traditional evolution algorithm because of the clonal selection strategy and clonal mutation strategy We apply the immune clonal selection algorithm to the process of modeling normal behavior We compare our algorithm with the algorithm which applies the genetic algorithm to intrusion detection and applies the negative selection algorithm of the artificial immune system to intrusion detection in the dataset kddcup99 The experiment results have shown that the rule set obtained by our algorithm can detect unknown attack behavior effectively and have higher detection rate and lower false positive rate.