Issue No.12 - December (2011 vol.10)
Jaehyuk Choi , Kyungwon University, Seongnam
Alexander W. Min , Intel Labs, Hillsboro
Kang G. Shin , The University of Michigan, Ann Arbor
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2010.262
Detecting misbehaving users in wireless networks is an important problem that has been drawing considerable attention. Even though there is a plethora of work on 802.11 wireless local area networks (WLANs), most existing schemes employ behavior-based anomaly detection, assuming that the backoff-time information of each transmitting node is available to the monitoring node. Unfortunately, it is practically infeasible to obtain the accurate backoff value chosen by other transmitting nodes because this MAC-layer information is not readily available. In this paper, we propose a practical way of pinpointing the misbehaving nodes without requiring access of hardware-level (e.g., backoff time) information in 802.11 WLANs. In contrast to most prior work, our scheme exploits the sequence of successfully received packets, which are readily observable at the access point. The distinct features of our scheme are that it 1) promptly detects a misbehaving node using a sequential hypothesis test, 2) performs well in realistic erroneous channel conditions due to its ability to accurately capture link heterogeneity, and 3) incurs negligible memory and computation overheads as it makes detection decisions based on runtime observations. The effectiveness of the proposed scheme is evaluated via extensive simulation as well as implementation, demonstrating its capability of accurately detecting nodess' selfish behavior in realistic 802.11 WLAN environments.
Network monitoring, IEEE 802.11, WLANs, passive online detection, driver-level solution, greedy behavior.
Jaehyuk Choi, Alexander W. Min, Kang G. Shin, "A Lightweight Passive Online Detection Method for Pinpointing Misbehavior in WLANs", IEEE Transactions on Mobile Computing, vol.10, no. 12, pp. 1681-1693, December 2011, doi:10.1109/TMC.2010.262