Unsupervised intrusion detection based on feature selection
Jian Wu · Computer Engineering and Applications Journal · 2011
In order to improve performances of intrusion detection system in terms of detection speed and detection rate,a novel unsupervised intrusion detection method based on Genetic Algorithm(GA) and feature selection mechanism is proposed.In the method,an improved GA is adopted as search strategy.On the other hand,the K-means clustering is used to classify the feature data,whose evaluation target is the ratio of the between-class scatter to the within-class scatter.Then,the optimal feature subset is found and applied to unsupervised intrusion detection.The experimental results show that the method can solve the feature selection problem of intrusion detection effectively,and it has a better detecting effect than unsupervised intrusion detection without feature selection.