An automatic and self-adaptive multi-layer data fusion system for WiFi attack detection
journal contributionposted on 06.02.2014 by Francisco Aparicio-Navarro, Kostas Kyriakopoulos, David Parish
Any type of content formally published in an academic journal, usually following a peer-review process.
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 ]
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