Building lightweight intrusion detection system using wrapper-based feature selection mechanisms |
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Authors: | Yang Jun-Li Wang Zhi-Hong Tian Tian-Bo Lu Chen Young |
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Affiliation: | aChina Mobile Research Institute, Beijing 100053, China;bPeking University Founder Technology College, Beijing 065001, China;cNational Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China;dChinese Academy of Sciences, Beijing 100190, China |
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Abstract: | Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and network infrastructures. How to build a lightweight IDS is a hot topic in network security. Moreover, feature selection is a classic research topic in data mining and it has attracted much interest from researchers in many fields such as network security, pattern recognition and data mining. In this paper, we effectively introduced feature selection methods to intrusion detection domain. We propose a wrapper-based feature selection algorithm aiming at building lightweight intrusion detection system by using modified random mutation hill climbing (RMHC) as search strategy to specify a candidate subset for evaluation, as well as using modified linear Support Vector Machines (SVMs) iterative procedure as wrapper approach to obtain the optimum feature subset. We verify the effectiveness and the feasibility of our feature selection algorithm by several experiments on KDD Cup 1999 intrusion detection dataset. The experimental results strongly show that our approach is not only able to speed up the process of selecting important features but also to yield high detection rates. Furthermore, our experimental results indicate that intrusion detection system with feature selection algorithm has better performance than that without feature selection algorithm both in detection performance and computational cost. |
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Keywords: | Network security Intrusion detection system Feature selection Modified RMHC Modified linear SVMs |
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