Title :
A clustering approach to wireless network intrusion detection
Author :
Zhong, Shi ; Khoshgoftaar, Taghi M. ; Nath, Shyarn V.
Author_Institution :
Dept. of Comput. Sci. & Eng., Florida Atlantic Univ., Boca Raton, FL
Abstract :
Intrusion detection in wireless networks has become an indispensable component of any useful wireless network security systems, and has recently gained attention in both research and industry communities due to widespread use of wireless local area networks (WLANs). This paper focuses on detecting intrusions or anomalous behaviors in WLANs with data clustering techniques. We first explore the security vulnerabilities of 802.11 or Wi-Fi networks and summarize the network traffic metrics that are important to model the security of wireless networks. Based on the metrics studied we propose a clustering-based intrusion detection approach and evaluate it on a real-world large wireless network traffic dataset. The evaluation results demonstrate the effectiveness of our proposed intrusion detection approach for wireless networks
Keywords :
pattern clustering; security of data; telecommunication security; telecommunication traffic; wireless LAN; 802.11 network; WI-FI networks; anomalous behaviors detection; clustering-based intrusion detection; data clustering; network traffic metrics; security vulnerabilities; wireless local area networks; wireless network intrusion detection; wireless network security systems; wireless network traffic dataset; Communication system security; Computer science; Computer security; Data security; Information security; Intrusion detection; Telecommunication traffic; Traffic control; Wireless application protocol; Wireless networks;
Conference_Titel :
Tools with Artificial Intelligence, 2005. ICTAI 05. 17th IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2488-5
DOI :
10.1109/ICTAI.2005.5