An Improvement of Payload-Based Intrusion Detection Using Fuzzy Support Vector Machine

Intrusion detection plays a very important role in network security system. It is proved to analyze the payload of network protocol and to model a payload-based anomaly detector (PAYL) can successfully detect outliers of network servers. This paper extends these works by applying a new noise-reduced fuzzy support vector machine (fSVM) to improve the detection rate. The new method named PAYL-fSVM employs reconstruction error based fuzzy membership function to reduce the noisy of the data and to solve the sharp boundary problem. Experimental results based on DARPA data set demonstrated that the proposed scheme can achieve higher detection rate at very low false positive rate than the original PAYL method.