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Principle components analysis and support vector machine based intrusion detection system.
| Content Provider | CiteSeerX |
|---|---|
| Author | Eid, Heba F. Darwish, Ashraf Hassanien, Aboul Ella Abraham, Ajith |
| Abstract | Abstract—Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and infrastructures. In this paper, we effectively introduced intrusion detection system by using Principal Component Analysis (PCA) with Support Vector Machines (SVMs) as an approach to select the optimum feature subset. We verify the effectiveness and the feasibility of the proposed IDS system by several experiments on NSL-KDD dataset. A reduction process has been used to reduce the number of features in order to decrease the complexity of the system. The experimental results show that the proposed system is able to speed up the process of intrusion detection and to minimize the memory space and CPU time cost. Keywords-Network security; Intrusion detection system; Feature selection; Support Vector Machines (SVMs); Principal |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Intrusion Detection System Support Vector Machine Principle Component Analysis Intrusion Detection Reduction Process Network Security Cpu Time Cost Optimum Feature Subset Keywords-network Security Experimental Result Necessary Component Id System Feature Selection Principal Component Analysis Protecting Network Resource Abstract Intrusion Detection System Proposed System Memory Space Nsl-kdd Dataset Several Experiment |
| Content Type | Text |
