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Despite the growing popularity of 802.11 wireless networks, users oftensuffer from connectivity problems and performance issues due to unstable radioconditions and dynamic user behavior among other reasons. Anomaly detection anddistinction are in the thick of major challenges that network managersencounter. Complication of monitoring the broaden and complex WLANs, that oftenrequires heavy instrumentation of the user devices, makes the anomaly detectionanalysis even harder. In this paper we exploit 802.11 access point usage dataand propose an anomaly detection technique based on Hidden Markov Model (HMM)and Universal Background Model (UBM) on data that is inexpensive to obtain. Wethen generate a number of network anomalous scenarios in OMNeT++/INET networksimulator and compare the detection outcomes with those in baseline approaches(RawData and PCA). The experimental results show the superiority of HMM andHMM-UBM models in detection precision and sensitivity.

802.11 Wireless Simulation and Anomaly Detection using HMM and UBM