Analysis about Performance of Multiclass SVM Applying in IDS

This paper presents a novel network intrusion detection approach with the Support Vector Machine embedded in and Kfold cross-validation method compounded for optimizing the attributes and SVM model. Compared with some representative machine learning method, online data experimental results show that this method can be used to reduce the rate of FalseNegatives in the intrusion detection system.

[1]  Gabriel Maciá-Fernández,et al.  Anomaly-based network intrusion detection: Techniques, systems and challenges , 2009, Comput. Secur..

[2]  Fan Da-peng.  Artificial Invasion Detection Technique , 2007 .

[3]  Dong Chun.  Detecting intrusions by using support vector machines , 2003 .

[4]  Liu Xin-zhe.  Application of artificial intelligence technigues to Intrusion Detection System , 2004 .

[5]  Teresa F. Lunt,et al.  Knowledge-based intrusion detection , 1989, [1989] Proceedings. The Annual AI Systems in Government Conference.

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1986, 1986 IEEE Symposium on Security and Privacy.

[8]  Tianqi Xu,et al.  Network Intrusion Detection Based on Support Vector Machine , 2009, 2009 International Conference on Management and Service Science.