Abstract
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. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes 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 Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.

Abstract

Abstract
In 802.11 Wireless Networks, detecting faulty equipment, poor radio conditions, and changes in user behavior through anomaly detection techniques is of great importance in network management. The traffic load and user movement on different access points (APs) in a wireless covered area vary with time, making these network management tasks harder. We intend to inspect the evolving structure of wireless networks and their inherent dynamics in order to provide models for anomaly detection. For this purpose we explore the temporal usage behavior of the network by applying various types of Hidden Markov Models. We observe the usage pattern of up to 100 APs in one week period in 2011 at the Faculty of Engineering of the University of Porto. The first step of this study consists of constructing various Hidden Markov Models from 802.11 AP usage data. We then apply statistical techniques for outlier detection and justify the presented outliers by inspecting the models' parameters and a set of HMM indicators. We finally introduce examples of wireless networks anomalous patterns based on the transitions between HMM states and provide an analysis of the entire set of APs under study.