Loughborough University
Revied - AutomaticBPA_Reduced04.pdf (308.28 kB)

A multi-layer data fusion system for Wi-Fi attack detection using automatic belief assignment

Download (308.28 kB)
conference contribution
posted on 2012-09-11, 07:59 authored by Francisco Aparicio-Navarro, Kostas KyriakopoulosKostas Kyriakopoulos, David Parish
Wireless networks are increasingly becoming susceptible to more sophisticated threats. An attacker may spoof the identity of legitimate users before implementing more serious attacks. Most of the current Intrusion Detection Systems (IDS) that employ multi-layer approach to help towards mitigating network attacks, offer high detection accuracy rate and low numbers of false alarms. Dempster-Shafer theory has been used with the purpose of combining beliefs of different metric measurements across multiple layers. However, an important step to be investigated remains open; this is to find an automatic and self-adaptive process of Basic Probability Assignment (BPA). This paper describes a novel BPA methodology able to automatically adapt its detection capabilities to the current measured characteristics, with a light weight process of generating a baseline profile of normal utilisation and without intervention from the IDS administrator. We have developed a multi-layer based application able to classify individual network frames as normal or malicious.


This work has been funded by the Engineering and Physical Sciences Research Council (EPSRC).



  • Mechanical, Electrical and Manufacturing Engineering


APARICIO-NAVARRO, F.J., KYRIAKOPOULOS, K.G. and PARISH, D.J., 2012. A multi-layer data fusion system for Wi-Fi attack detection using automatic belief assignment. World Congress on Internet Security (WorldCIS), Guelph, Ontario, Canada, June 10-12, University of Guelph, Ontario, Canada, pp.45-50.




  • AM (Accepted Manuscript)

Publication date



© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.




  • en