Application of Feature Selection and Fuzzy ARTMAP to Intrusion Detection

This paper proposes a novel approach for intrusion detection and diagnosis. The proposed approach uses Sequential Backward Floating Search for feature selection and fuzzy ARTMAP for detection and diagnosis of attacks. The optimal vigilance parameter for the fuzzy ARTMAP is chosen using a genetic algorithm. The reduced set of features decreases the computation time by 0.789 s. A classification rate of 100% and 99.89% is obtained for the detection stage and diagnosis stage, respectively.

[1]  Sankar K. Pal,et al.  Self-organizing neural network as a fuzzy classifier , 1994, IEEE Trans. Syst. Man Cybern..

[2]  Jie Yang,et al.  Degree prediction of malignancy in brain glioma using support vector machines , 2006, Comput. Biol. Medicine.

[3]  Chunlin Zhang,et al.  Intrusion detection using hierarchical neural networks , 2005, Pattern Recognit. Lett..

[4]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[5]  H. Javitz,et al.  Detecting Unusual Program Behavior Using the Statistical Component of the Next-generation Intrusion Detection Expert System ( NIDES ) 1 , 1997 .

[6]  Pavel Paclík,et al.  Adaptive floating search methods in feature selection , 1999, Pattern Recognit. Lett..

[7]  W. Punch,et al.  Feature Extraction Using Genetic Algorithms , 1997 .

[8]  G. Carpenter.  Default ARTMAP , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[9]  Manabu Kotani,et al.  Feature Extraction Using Genetic Algorithms , 1999 .

[10]  Thomas Gärtner,et al.  WBCsvm: Weighted Bayesian Classification based on Support Vector Machines , 2001, ICML.

[11]  Peter G. Neumann,et al.  EMERALD: Event Monitoring Enabling Responses to Anomalous Live Disturbances , 1997, CCS 2002.

[12]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[13]  Richard J. Enbody,et al.  Further Research on Feature Selection and Classification Using Genetic Algorithms , 1993, ICGA.

[14]  Hervé Debar,et al.  An application of a recurrent network to an intrusion detection system , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[15]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[16]  Ajith Abraham,et al.  Feature deduction and ensemble design of intrusion detection systems , 2005, Comput. Secur..

[17]  Gail A. Carpenter,et al.  Default ARTMAP 2 , 2007, 2007 International Joint Conference on Neural Networks.

[18]  Hervé Debar,et al.  A neural network component for an intrusion detection system , 1992, Proceedings 1992 IEEE Computer Society Symposium on Research in Security and Privacy.

[19]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[20]  Koral Ilgun,et al.  USTAT: a real-time intrusion detection system for UNIX , 1993, Proceedings 1993 IEEE Computer Society Symposium on Research in Security and Privacy.

[21]  Erik D. Goodman,et al.  Genetic Algorithm Optimized Feature Transformation - A Comparison with Different Classifiers , 2003, GECCO.

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[23]  Andrew H. Sung,et al.  Feature Selection for Intrusion Detection with Neural Networks and Support Vector Machines , 2003 .

[24]  Andrew H. Sung,et al.  Intrusion detection using an ensemble of intelligent paradigms , 2005, J. Netw. Comput. Appl..

[25]  Kristopher Kendall,et al.  A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems , 1999 .

[26]  Elbert Marais,et al.  Predicting Global Internet Instability Caused by Worms using Neural Networks , 2006 .