A Cooperative Network Intrusion detection Based on Fuzzy SVMs

Abstract

There is a large number of noise in the data obtained from network, which deteriorates intrusion detection performance. To delete the noise data, data preprocessing is done before the construction of hyperplane in support vector machine (SVM). By introducing fuzzy theory into SVM, a new method is proposed for network intrusion detection.  Because the attack behavior is different for different network protocol, a different fuzzy membership function is formatted, such that for each class of protocol there is a SVM. To implement this approach, a fuzzy SVM-based cooperative network intrusion detection system with multi-agent architecture is presented. It is composed of three types of agents corresponding to TCP, UDP, and ICMP protocols, respectively. Simulation experiments are done by using KDD CUP 1999 data set, results show that the training time is significantly shortened, storage space requirement is reduced, and classification accuracy is improved.