Detecting anomalies in WLAN using discrimination algorithm

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. Anomaly detection is fast becoming a key element of intrusion detection systems, in which perturbations from normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. Accurately detecting such anomalies has become an important problem for the network community. We need to distinguish anomalies that change the traffic either abruptly or slowly. 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 data set. Hence we developed a discrimination algorithm using correlation coefficient to detect anomalies in the Wireless traffic and demonstrated the effectiveness.