scholarly journals Strategy for Wireless Local Area Networks Planning and Performance Evaluation through Bayesian Networks as Computational Intelligence Approach

10.5772/10066 ◽  
2010 ◽  
Author(s):  
Jasmine Araujo ◽  
Josiane Rodrigues ◽  
Simone Fraiha ◽  
Herminio Gomes ◽  
Gervsio Cavalcante ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Bayesian Networks ◽  
Computational Intelligence ◽  
Local Area Networks ◽  
Local Area ◽  
Wireless Local Area Networks ◽  
And Performance ◽  
Intelligence Approach

scholarly journals 802.11 wireless simulation and anomaly detection using HMM and UBM

SIMULATION ◽  
2020 ◽  
Vol 96 (12) ◽  
pp. 939-956 ◽  
Author(s):  
Anisa Allahdadi ◽  
Ricardo Morla ◽  
Jaime S Cardoso
Keyword(s):  
Anomaly Detection ◽  
User Behavior ◽  
Local Area Networks ◽  
Principal Component ◽  
Access Point ◽  
Local Area ◽  
Wireless Local Area Networks ◽  
Network Simulator ◽  
Detection Analysis ◽  
And Performance

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.

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