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A parameter selection approach for mixtures of kernels using immune evolutionary algorithm and its application to IDSs  

作  者:Yang, Chun[1];Yang, Haidong[1];Deng, Feiqi[1]

作者单位:S China Univ Technol, Coll Automat Sci & Engn, Guangzhou 510640, Peoples R China

会议名称:CIS: 2007 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, PROCEEDINGS

摘  要:Supervised anomaly intrusion detection systems (IDSs) based on Support Vector Machines (SVMs) classification technique have attracted much more attention today. In these systems, the characteristics of kernels have great influence on learning and prediction results for IDSs. However selecting feasible parameters can be time-consuming as the number of parameters and the size of the dataset increase. In this paper, an immune evolutionary based kernel parameter selection approach is proposed. Through the simulation of the denial of service attacks in mobile ad-hoc networks (MANETs), the result dataset is used for comparing the prediction performance using different types of kernels. At the same time, the parameter selection efficiency of the proposed approach is also compared with the differential evolution algorithm.

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