802.11 wireless simulation and anomaly detection using HMM and UBM. (December 2020)

• Record Type: Journal Article
• Authors: Allahdadi, Anisa; Morla, Ricardo; Cardoso, Jaime S
• Is Part Of: Simulation. Volume 96:Number 12(2020)
• Page Start: 939
• Page End: 956
• Publication Date: 2020-12
• Subjects: 802.11 wireless network -- network management -- anomaly detection -- Hidden Markov Model -- Universal Background Model -- network simulation -- OMNeT++/INET; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549720958480
• Languages: English
• ISSNs: 0037-5497

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.