Outlier Detection in 802.11 Wireless Access Points Using Hidden Markov Models

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Anisa Allahdadi (Author) Other; Ricardo Morla (Author) FEUP; Jaime S Cardoso (Author) FEUP

Conference proceedings International

Title: 2014 7TH IFIP WIRELESS AND MOBILE NETWORKING CONFERENCE (WMNC)

Pages: 1-8

7th IFIP Wireless and Mobile Networking Conference (WMNC)

Vilamoura, PORTUGAL, MAY 20-22, 2014

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-009-S22

DOI: 10.1109/wmnc.2014.6878860

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8