Abstract — Security and privacy of a system is compromised, when an intrusion happens. Intrusion Detection System (IDS) plays vital role in network security as it detects various types of attacks in network. So here, we are going to propose Intrusion Detection System using data mining technique: SVM (Support Vector Machine). Here, Classification will be done by using SVM and verification regarding the effectiveness of the proposed system will be done by conducting some experiments using NSL-KDD Cup’99 dataset which is improved version of KDD Cup’99 data set. The SVM is one of the most prominent classification algorithms in the data mining area, but its drawback is its extensive training time. In this proposed system, we have carried out some experiments using NSL-KDD Cup’99 data set. The experimental results show that we can reduce extensive time required to build SVM model by performing proper data set pre-processing. Also when we do proper selection of SVM kernel function such as Gaussian Radial Basis Function, attack detection rate of SVM is increased and False Positive Rate (FPR) is decrease
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