Network intrusion detection based on simultaneous optimization of features selection and parameters of support vector machine
Fan Aiwa · Journal of Beijing Jiaotong University · 2013
In order to improve network intrusion detection rate, this paper proposed a network intrusion detection algorithm based on simultaneous optimization of feature selection and SVM parameters which used the relationship between the feature selection and SVM parameters. Firstly, the network intrusion detection rate as the objection function to built mathematical model which the constraint conditions were the feature and SVM parameters. Secondly, the genetic algorithm was used to get the optimal features and SVM parameters. Lastly, the performance of the proposed algorithm was tested by KDD 1999 data. The results showed that the proposed algorithm could select the optimal features and SVM parameters to improve the network intrusion detection rate and detection speed compared with other network intrusion detection algorithms.