802.11 wireless simulation and anomaly detection using HMM and UBM

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Anisa Allahdadi (Autor) Outra; Ricardo Morla (Autor) FEUP; Jaime S. Cardoso (Autor) FEUP

Revista

Título: Simulation

Vol. 96 Nº 12

Páginas: 939-956

ISSN: 0037-5497

Editora: SAGE

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00S-RA1

DOI: 10.1177/0037549720958480

Abstract (EN): 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. © The Author(s) 2020.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 18