802.11 wireless simulation and anomaly detection using HMM and UBM

Author:

Allahdadi Anisa1ORCID,Morla Ricardo1,Cardoso Jaime S1

Affiliation:

1. INESC TEC and Faculty of Engineering, University of Porto, Portugal

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.

Funder

European Regional Development Fund

Fundação para a Ciência e a Tecnologia

Publisher

SAGE Publications

Subject

Computer Graphics and Computer-Aided Design,Modelling and Simulation,Software

Reference33 articles.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Domain adaptive deep semi-supervised transfer learning for anomaly detection in OpenWiFi;2024 International Wireless Communications and Mobile Computing (IWCMC);2024-05-27

2. On Augmented Intelligence and Performance Anomaly Detection in Unlabeled OpenWiFi Data;ICC 2023 - IEEE International Conference on Communications;2023-05-28

3. Hidden Markov models on a self-organizing map for anomaly detection in 802.11 wireless networks;Neural Computing and Applications;2021-01-02

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