An Automatic and Self-Adaptive Multi-Layer Data Fusion System for WiFi Attack Detection.pdf (6.19 MB)

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

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journal contribution
posted on 06.02.2014, 16:36 by Francisco Aparicio-Navarro, Kostas Kyriakopoulos, David Parish
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. Copyright © 2013 Inderscience Enterprises Ltd.


This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/H005005/1 ]



  • Mechanical, Electrical and Manufacturing Engineering


APARICIO-NAVARRO, F.J., KYRIAKOPOULOS, K.G. and PARISH, D.J., 2013. An automatic and self-adaptive multi-layer data fusion system for WiFi attack detection. International Journal of Internet Technology and Secured Transactions, 5 (1), pp. 42 - 62.


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This article was published in the journal, International Journal of Internet Technology and Secured Transactions [© Inderscience] and the definitive version is available at: