Intrusion Detection Based on Clustering Quantum Genetic Algorithm
Linlin Wang, Kaiwei Zhu · Jisuanji gongcheng · 2009
For traditional intrusion detection algorithms,the lack of self-adaptive and intelligent has become increasingly prominent when they cope with unknown attacks.This paper presents a new unsupervised,adaptive detection algorithm,Clustering Quantum Genetic Algorithm(CQGA).This algorithm employs the Euclidean distance between the samples as the standard of similarity measurement.Furthermore,it can auto-classify the sample sets in the unsupervised condition through quantum genetic algorithm finding the cluster center.Experimental result indicates that the algorithm can classify the test data set accurately,and solve the problem of self-adapting and intelligent effectively.