Passive online wireless LAN health monitoring from a single measurement point
ACM SIGMOBILE Mobile Computing and Communications Review
Identifying 802.11 traffic from passive measurements using iterative Bayesian inference
IEEE/ACM Transactions on Networking (TON)
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In this paper, we propose two online algorithms to detect 802.11 traffic from packet-header data collected passively at a monitoring point. These algorithms have a number of applications in \emph{realtime} wireless LAN management, for instance, in detecting unauthorized access points and detecting/predicting performance degradations. Both algorithms use sequential hypothesis tests, and exploit fundamental properties of the 802.11 CSMA/CA MAC protocol and the half duplex nature of wireless channels. They differ in that one requires training sets, while the other does not. We have built a system for online wireless-traffic detection using these algorithms and deployed it at a university gateway router. Extensive experiments have demonstrated the effectiveness of our approach: the algorithm that requires training provides rapid detection and is extremely accurate (the detection is mostly within 10 seconds, with very low false positive and false negative ratios); the algorithm that does not require training detects $60\%$-$76\%$ of the wireless hosts without any false positives; both algorithms are light-weight, with computation and storage overhead well within the capability of commodity equipment.