DefinePK

Abstract

In this paper, we suggest to exploit the framework for detecting anomalies in Wireless Local Area Networks (WLAN) using Rough Set Theory (RST). With the expansion of wireless network there is a challenge to compete with the intruders who can easily break into the system. So it becomes a necessity to device systems or algorithms that can not only detect intrusion but can also improve the detection rate. In this paper we propose an intrusion detection system that uses rough set theory for feature selection, which is extraction of relevant attributes from the entire set of attributes called minimal set. The extracted features are used by Naïve Bayesian classifier model to learn and test respectively. The simulation results with Kyoto2006+ data set demonstrate that our proposed method achieves the increasing performance for intrusion detection.