Abstract: In order to improve performances of intrusion detection system in terms of detection speed and detection rate,itis necessary to apply feature selection in intrusion detection system. Firstly,an efficient search procedure based on immune memory and genetic algorithm (IMGA) was proposed. Then, support vector machine (SVM) based on wrapper feature evaluation methods was surveyed,in order to improve the feature selection performance of unbalanced datasets. We used the conformal transformation and Riemannian metric to modify kernel function, and reconstructed a new Modified Kernel SVM (MKSVM). Finally, the simulation experimental results show that this approach can improve the process of selecting important features, and has better feature selection ability on the unbalanced data. Furthermore, the experiments indicate that intrusion detection system with this feature selection algorithm has better performanccs than that without feature selection algorithm.

Key words: Feature selection, Intrusion detection, Genetic algorithm, Support vector machine, Modified kernel