Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12062
Title: Comparison of different fusion approaches for network intrusion detection using ensemble of RBFNN
Authors: Chan, APF
Ng, WWY
Yeung, DS
Tsang, ECC
Keywords: Computer networks
Inference mechanisms
Pattern classification
Quality of service
Radial basis function networks
Security of data
Issue Date: 2005
Publisher: IEEE
Source: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, 18-21 August 2005, Guangzhou, China, v. 6, p. 3846-3851 How to cite?
Abstract: The information technology has been adopted to solve problems in network intrusion detection system (IDS) and many approaches have been proposed to tackle the information security problems of computer networks, especially the denial of service (DoS) attacks. The multiple classifier system (MCS) is one of the approaches that has been adopted in the detection of DoS attacks recently. For a MCS to perform better than a single classifier, it is crucial to select the appropriate fusion strategies. Majority vote, average, weighted sum, weighted majority vote, neural network and Dempster-Shafer combination are the fusion strategies that have been widely adopted. The selection of the fusion strategy for a MCS in DoS problem varies widely. In this paper, a comparative study on adopting different fusion strategies for a MCS in DoS problem is provided.
URI: http://hdl.handle.net/10397/12062
ISBN: 0-7803-9091-1
DOI: 10.1109/ICMLC.2005.1527610
Appears in Collections:Conference Paper

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