Intrusion Detection System Using Data Mining Technique : Support Vector Machine

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 NSLKDD 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. Keywords— Classification, Intrusion Detection System (IDS), Kernel Function, NSLKDD, Pre-processing, Support Vector Machine (SVM)

[1]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[2]  Zhi-Hua Hu,et al.  An Incremental SVM for Intrusion Detection Based on Key Feature Selection , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[3]  Zehua Chen,et al.  An adaptive real-time intrusion detection system using sequences of system call , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

[4]  Chunhua Gu,et al.  A Rough Set and SVM Based Intrusion Detection Classifier , 2009, 2009 Second International Workshop on Computer Science and Engineering.

[5]  Rung Ching Chen,et al.  Using Rough Set and Support Vector Machine for Network Intrusion Detection System , 2009, 2009 First Asian Conference on Intelligent Information and Database Systems.

[6]  Balaraman Ravindran,et al.  Adaptive network intrusion detection system using a hybrid approach , 2012, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[7]  Salvatore J. Stolfo,et al.  A framework for constructing features and models for intrusion detection systems , 2000, TSEC.

[8]  Michael Neethu.  Classification of Intrusion Detection Dataset using machine learning Approaches , 2012 .

[9]  M. McNeese,et al.  idsNETS: An experimental platform to study situation awareness for intrusion detection analysts , 2012, 2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support.

[10]  Tamas Abraham.  IDDM: Intrusion Detection Using Data Mining Techniques , 2001 .

[11]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[12]  Ashraf Darwish,et al.  Principle components analysis and Support Vector Machine based Intrusion Detection System , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[13]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[14]  Shingo Mabu,et al.  An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[15]  Raouf Boutaba,et al.  Dirichlet-Based Trust Management for Effective Collaborative Intrusion Detection Networks , 2011, IEEE Transactions on Network and Service Management.

[16]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.