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