Principle components analysis and Support Vector Machine based Intrusion Detection System

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.