期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2011
卷号：8
期号：5
出版社：IJCSI Press
摘要：Abstract This paper presents a fast learning algorithm using Adaboost ensemble with simple genetic algorithms (GAs) for intrusion detection systems. Unlike traditional approaches using Adaboost algorithms, it proposed a Genetic Algorithm post optimization procedure for the found classifiers and their coefficients removing the redundancy classifiers which cause higher error rates and leading to shorter final classifiers and a speedup of classification. This approach has been implemented and tested on the NSL-KDD dataset and its experimental results show that the method reduces the complexity of computation, while maintaining the high detection accuracy. Moreover, the method improves the processing time, so it is especially appealing for the real-time processing of the intrusion detection system.
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