Abstract
In this paper, we present a statistical pattern mining approach to model the usage patterns of authenticated users to identify wireless network intruders. Considering users activities in terms of ICMP packets sent, DNS query requests and ARP requests, in this paper a statistical approach is presented to consolidate authenticated users activities over a period of time and to derive a separate feature vector for each activity. The proposed approach also derives a local threshold for each category of network data analyzed. The learned features and local threshold for each category of data is used during detection phase of the system to identify intruders in the network. The novelty of the proposed method lies in the elimination of redundant and irrelevant features using PCA that often reduce detection performance both in terms of efficiency and accuracy. This also leads our proposed system to be light-weight and deployable in real-time environment.
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Haldar, N.A.H., Abulaish, M., Pasha, S.A. (2012). A Statistical Pattern Mining Approach for Identifying Wireless Network Intruders. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31513-8_14
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DOI: https://doi.org/10.1007/978-3-642-31513-8_14
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