期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2006
卷号：6
期号：2B
页码：208-208~218
出版社：International Journal of Computer Science and Network Security
摘要：Most intrusion detection systems (IDS) are based on a single algorithm that is designed to either model the normal behaviour patterns or attack signatures in network data traffic. Most often, these systems fail to provide adequate alarm capability that reduces false positive and false negative rates. We here propose a double multiple-model approach capable of enhancing the overall performance of IDS. In a first step, every group of identical intrusion detection models are combined independently of the rest of the groups to produce a fused intrusion detection model. Then all the fused models are fused to produce the final intrusion detection model. Our IDS model adopted three reasoning methods: Naive Bayesian, Neural Nets, and Decision Trees. We used Darpa attack taxonomy and the KDD Intrusion Detection Dataset to demonstrate the working of our IDS model.
