Anomaly Based Intrusion Detection in Wlan Using Discrimination Algorithm Combined with Naïve Bayesian Classifier

The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network security – especially for building a good network defense infrastructure. Due to the wide popularity of Wireless Networks tremendous applications are emerging and Wireless Local Area Network (WLAN) has gained attention by both research and industry communities. The wide spread deployment of WLAN has also brought new challenges to security and privacy. We need to distinguish anomalies that change the traffic either abruptly or slowly. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply correlation coefficient based learning approach for detecting anomalies in wireless Local area network. While a good amount of research has been done for fixed wired networks, not much research has been done in this area for wireless networks due to lack of a good dataset. Hence we developed a discrimination algorithm using correlation coefficient to detect anomalies combined with Naïve Bayesian classifier in the Wireless traffic and demonstrated the effectiveness using Kyoto 2006+ datasets. An experiment is carried out in order to evaluate performance based on accuracy, detection rate and false positive rate of the classification scheme. Results and analysis shows that the proposed approach has enhanced the detection rate with minimum false positive rates.