Hybrid Fuzzy Based Intrusion Detection System for Wireless Local Area Networks (HFIDS)

The drawback of the anomaly based intrusion detection in a wireless network is the high rate of false positive. By designing a hybrid intrusion detection system can solve this by connecting a misuse detection module to the anomaly detection module. In this paper, we propose to develop a hybrid intrusion detection system for wireless local area networks, based on Fuzzy logic. In this Hybrid Intrusion Detection system, anomaly detection is performed using the Bayesian network technique and misuse detection is performed using the Support Vector Machine (SVM) technique. The overall decision of system is performed by the fuzzy logic. For anomaly detection using Bayesian network, each node has a monitoring agent and a classifier within it for its detection and a mobile agent for information collection. The anomaly is measured based on the naive Bayesian technique. For misuse detection using SVM, all the data that lie within the hyperplane are considered to be normal whereas the data that lie outside the hyperplane are considered to be intrusive. The outputs of both anomaly detection and misuse detection modules are applied by the fuzzy decision rules to perform the final decision making.