<p>Wireless security is becoming an important area of product research and development. Wireless Intrusion detection Systems are commonly used in WLAN network for detecting wireless attacks. Classifiers are commonly used as detectors in these systems. Finding an efficient classifier as well selecting best set of features becomes very important for implementing these intrusion detection systems. In this paper, we are finding optimital set of features from collected WLAN data using a Ranking Algorithm technique. Then with the aid of different data mining techniques such as K-Means, self organizing map and decision tree, these features are analyzed and the performance comparison is carried out. © 2010 ACM.</p>
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@6e1f9d8 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@5dd9d76f Through org.apache.xalan.xsltc.dom.DOMAdapter@8cee88c; Conference Code:82507
A. M. Nambiar, Asha Vijayan, and Nandakumar, A., “Wireless intrusion detection based on different clustering approaches”, in Proceedings of the 1st Amrita ACM-W Celebration of Women in Computing in India, A2CWiC'10, Coimbatore, 2010.