Network Intrusion Detection Based on Improved Proximal SVM
Chengjie Gu, Shunyi Zhang, Xiaozhen Xue · INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences · 2011
Intrusion detection is one of the most essential factors for security infrastructures in network environments, and it is widely used in detecting, identifying and tracking the intruders. To solve the drawback of the SVM algorithm to meet the requirements of the network intrusion detection, we propose network intrusion detection based on improved proximal SVM. Experiment results illustrate the formulation of PSVM greatly simplifies the problem with considerably faster computational time than SVM for network intrusion. This method also can shorten the training time and improve detection performance by improved kernel function.