On RSN-oriented wireless intrusion detection
OTM'07 Proceedings of the 2007 OTM confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part II
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The increasing reliance upon wireless networks has put tremendous emphasis on wireless network security. While considerable attention has been given t o data mining for intrusion detection i n wired networks, limited focus has been devoted to data mining for intrusion detection in wireless networks. This study presents a clustering approach with tracers and ezpert analysis for intrusion detection in a real-world wireless network. Security vulnerabilities of 802.11 wireless networks are investigated, leading to a summary of network trafic metrics relevant to modeling the security of wireless networks. The proposed approach utilizes a simple distance-based heuristic measure to label clusters as either normal or intrusive. The classification of network trafic instances is further enhanced with the aid of tracers, i.e., a small set of instances with known labels - normal or intrusive. Our study demonstrates the usefilness and promise of the proposed approach, laying the groundwork for a clustering-based framework for intrusion detection i n wireless computer networks.