An automatic and self-adaptive multi-layer data fusion system for WiFi attack detection

Authors:
Francisco J. Aparicio-Navarro;Konstantinos G. Kyriakopoulos;David J. Parish
Affiliations:
School of Electronic, Electrical and System Engineering, Loughborough University, Loughborough, LE11 3TU, UK;School of Electronic, Electrical and System Engineering, Loughborough University, Loughborough, LE11 3TU, UK;School of Electronic, Electrical and System Engineering, Loughborough University, Loughborough, LE11 3TU, UK
Venue:
International Journal of Internet Technology and Secured Transactions
Year:
2013

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Abstract

Wireless networks are becoming susceptible to increasingly more sophisticated threats. Most of the current intrusion detection systems IDSs that employ multi-layer techniques for mitigating network attacks offer better performance than IDSs that employ single layer approach. However, few of the current multi-layer IDSs could be used off-the-shelf without prior thorough training with completely clean datasets or a fine tuning period. 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, without intervention from the IDS administrator. We have developed a multi-layer-based application able to classify individual network frames as normal or malicious with perfect detection accuracy.