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|Title:||Distributed Detection of Network Intrusions Based on a Parametric Model|
|Citation:||IEEE International Conference on Systems, Man and Cybernetics, 2008 - Proceedings: pp.2069-2074|
|Conference Name:||IEEE International Conference on Systems, Man and Cybernetics (2008 : Singapore)|
|Yan-guo Wang, Xi Li, and Weiming Hu|
|Abstract:||With the increasing requirements of fast response and privacy protection, how to detect network intrusions in a distributed architecture becomes a hot research area in the development of modern information security systems. However, it is a challenge to build such a system, given the difficulties brought by the mixed-attribute property of network connection data and the constraints on network communication. In this paper, we present a framework for distributed detection of network intrusions based on a parametric model. The parametric model can explicitly reflect the distributions of different intrusion types and handle the mixed-attribute data naturally. Based on the model, we can generate an accurate global intrusion detector with a very low cost of communication among the distributed detection sites, and no sharing of original network data is needed. Experimental results demonstrate the advantages of the proposed framework in the distributed intrusion detection application.|
|Keywords:||Distributed detection; machine learning; information security|
|Appears in Collections:||Computer Science publications|
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