Support Vector Machines Improved by Artificial Immunisation Algorithm for Intrusion Detection

In this paper, a new intrusion detection method based on support vector machines improved by artificial immunization algorithm is presented. Support vector machines (SVM) has been well recognized as a powerful computational tool for problems with nonlinearity had high dimensionalities. Right setting parameters are very crucial to learning results and generalization ability of SVM. But empirical parameters are used frequently in SVM RFE, this has hampered its efficiency in practical application. Artificial immunisation algorithm (AIA) is a new intelligent algorithm which integrates global search with local search, and can effectively overcome the prematurity and slow convergence speed of traditional genetic algorithm. To improve the capability of the SVM classifier, The artificial immunisation algorithm is applied to optimize the parameter of SVM in this paper. The experimental result shows that the intrusion detection based on support vector machines improved by artificial immunisation algorithm can give higher recognition accuracy than the general SVM.