A novel intrusion detection method based on improved SVM by combining PCA and PSO
Hui Wang, Guiling Zhang, E. Mingjie, Na Sun · Wuhan University Journal of Natural Sciences · 2011
The paper presents an improved support vector machine (SVM) by combining principal component analysis (PCA) and particle swarm optimization (PSO). Then, the improved SVM is applied to the intrusion detection system (IDS) to improve the detection rate. First, PCA is used to reduce the dimension of feature vectors. Second, we use the PSO algorithm to optimize the punishment factor C and kernel parameters σ in SVM. The experimental results indicate that the intrusion detection rate (97.752 8%) of improved SVM by combining PCA and PSO is higher than those (95.635 5%) of PSO-SVM and those (90.476 2%) of standard SVM with KDD Cup 1999 data set.