A New Feature Extraction Method of Intrusion Detection
Zhu Xiaorong, Wang Dianchun, YE Chang-guo · 2009
The paper uses kernel principal component analysis to extract features from the intrusion detection training samples. The method extracts features and reduces the dimensions very effectively. In addition, we make use of RSVM method into nonlinear proximal SVM. It can reduce the computation requirements of the kernel matrix. The combination of the above two methods improve the training speed and classification effect.