Improvments of Payload-based Intrusion Detection Models by Using Noise Against Fuzzy SVM
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 at lower false positive rate. The new noisy against fuzzy SVM is applied to analyzing 1-gram, 2-grams and 2v-grams distribution classification of network payloads, which constructs three different intrusion detection models, respectively.