A novel hybrid KPCA and SVM with GA model for intrusion detection

作者：Kuang, Fangjun; Xu, Weihong^*; Zhang, Siyang

来源：Applied Soft Computing, 2014, 18: 178-184.

DOI：10.1016/j.asoc.2014.01.028

• Intrusion detection
• Kernel principal component analysis
• Kernel function
• Support vector machines
• Genetic algorithm

A novel support vector machine (SVM) model combining kernel principal component analysis (KPCA) with genetic algorithm ( GA) is proposed for intrusion detection. In the proposed model, a multi-layer SVM classifier is adopted to estimate whether the action is an attack, KPCA is used as a preprocessor of SVM to reduce the dimension of feature vectors and shorten training time. In order to reduce the noise caused by feature differences and improve the performance of SVM, an improved kernel N-RBF) is proposed by embedding the mean value and the mean square difference values of feature attributes in RBF kernel function. GA is employed to optimize the punishment factor C, kernel parameters sigma and the tube size epsilon of SVM. By comparison with other detection algorithms, the experimental results show that the proposed model performs higher predictive accuracy, faster convergence speed and better generalization.

• 出版日期2014-5
• 单位南京理工大学; 长沙理工大学