Ensemble of one-class classifiers for network intrusion detection system

Proceedings - The 4th International Symposium on Information Assurance and Security, IAS 2008

Volumn , Issue , 2008, Pages 180-185

  Zainal, Anazida ^a   Maarof, Mohd Aizaini ^a   Shamsuddin, Siti Mariyam ^a   Abraham, Ajith ^b  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

AGGLOMERATION; ALARM SYSTEMS; CLASSIFIERS; COMPUTER CRIME; COMPUTER NETWORKS; COMPUTER PROGRAMMING; DATA STRUCTURES; FUZZY INFERENCE; FUZZY SYSTEMS; GENETIC ALGORITHMS; GENETIC PROGRAMMING; INTERNET; LEARNING ALGORITHMS; LEARNING SYSTEMS; LINEAR PROGRAMMING; SECURITY OF DATA; SIGNAL DETECTION;

ADAPTIVE NEURAL FUZZY INFERENCE SYSTEMS; CLASSIFICATION TREES; DETECTION ACCURACIES; EMPIRICAL RESULTS; ENSEMBLE LEARNINGS; ENSEMBLE MODELS; FALSE ALARM RATES; HIGH ACCURACIES; INDIVIDUAL MODELS; INTRUSION DETECTION SYSTEMS; LEARNING PARADIGMS; LINEAR GENETIC PROGRAMMINGS; MACHINE LEARNING TECHNIQUES; NETWORK INTRUSION DETECTION SYSTEMS; NETWORK TRAFFICS; RANDOM FORESTS;

INTRUSION DETECTION;

EID: 55349101281     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IAS.2008.35     Document Type: Conference Paper

References (22)

1. S. Mukkamala, A.H. Hung and A. Abraham, "Intrusion Detection Using an Ensemble of Intelligent Paradigms." Journal of Network and Computer Applications, Vol. 28(2005), 167-182.
2. J.W. Kim, "Integrating Artificial Immune Algorithms for Intrusion Detection." PhD Thesis, Department of Computer Science, University College of London, 2002.
3. A.H. Sung and S. Mukkamala, "The Feature Selection and Intrusion Detection Problems." Proceedings of Advances in Computer Science - ASIAN 2004: Higher-Level Decision Making. 9th Asian Computing Science Conference. Vol. 3321(2004), 468-482.
4. S. Chebrolu, A. Abraham, and J.P. Thomas, "Feature Deduction and Ensemble Design of Intrusion Detection Systems." International Journal of Computers and Security, Vol. 24, Issue 4, (June 2005), 295-307.
5. S. Mukkamala, A.H. Sung and A. Abraham, "Modeling Intrusion Detection Systems Using Linear Genetic Programming Approach." LNCS 3029, Springer Hiedelberg, 2004, pp. 633-642.
6. A. Abraham and R. Jain, "Soft Computing Models for Network Intrusion Detection Systems." Soft Computing in Knowledge Discovery: Methods and Applications, Springer Chap 16, 2004, 20pp.
7. A. Abraham, C. Grosan, and C.M. Vide, "Evolutionary Design of Intrusion Detection Programs." International Journal of Network Security, Vol. 4, No. 3, 2007, pp. 328-339.
8. J.V., Hansen, P.B. Lowry, R.D. Meservy and D.M. McDonald, "Genetic Programming for Prevention of Cyberterrorism through Dynamic and Evolving Intrusion Detection." Journal of Decision Support Systems Vol. 43, 2007, pp. 1362-1374.
9. Koza, J.R. and Poli, R. 2003. A Genetic Programming Tutorial. http://www.genetic-programming.com/jkpdf/burke2003tutorial.pdf
10. K.M. Faraoun and A.Boukelif, "Genetic Programming Approach for Multi-Category Pattern Classification Applied to Network Intrusions Detection." International Journal of Computational Intelligence Vol. 3, No. 1(2006)79-90.
11. A.N. Toosi, and M. Kahani, "A new approach to intrusion detection based on a evolutionary soft computing model using neuro-fuzzy classifiers." Journal of Computer Communications, Vol. 30, 2007, pp. 2201-2212.
12. J.R. Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System." IEEE Transactions on Systems, Man and Cybernetics, Vol. 23, No. 3, May 1993, pp. 665-685.
13. M.S. Abadeh, J. Habibi and C. Lucas, "Intrusion Detection Using a Fuzzy Genetics-based Learning Algorithm." Journal of Network and Computer Applications Vol. 30, 2007, pp. 414-428.
14. L. Breimann, 2001, "Random Forests." Journal of Machine Learning, Kluwer Academic, Netherland, Vol. 45, 2001, pp. 5-32.
15. J. Zhang, and M. Zulkernine, 2006. A Hybrid Network Intrusion Detection Technique Using Random Forests. In Proceedings of the IEEE First International Conference on Availability, Reliability and Security (ARES'6).
16. th. IEEE Conference on Tools with Artificial Intelligence. 2007, pp. 310-317.
17. P. Xu, and F. Jelinek, " Random Forests and the Data Sparseness Problem in Language Modeling." Journal of Computer Speech and Language, Vol. 21, Issue l(Jan 2007) pp. 105-152.
18. J. Peters, B. De Baets, N.E.C. Verhoest, R. Samson, S. Degroeve, P. De Becker and W. Huybrechts, "Random Forests as a Tool for Ecohydrological Distribution Modelling." Journal of Ecological Modelling, Vol. 207, Issue 2-4, October 2007, pp. 304-318.
19. B. Lariviere, and D. Van den Poel, "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques." Journal of Expert Systems with Applications, Vol. 29, Issue 2, (August 2005) pp. 472-482.
20. S.K. Dong, M.L. Sang, and S.P. Jong, "Building Lightweight Intrusion Detection System Based on Random Forest." LNCS 3973, Springer-Verlag, Berlin Heidelberg (2006) pp. 224-230.
21. A. Zainal, M.A. Maarof and S.M. Shamsuddin, "Feature Selection Using Rough-DPSO in Anomaly Detection." LNCS 4705, Part 1 Springer Hiedelberg (2007) pp. 512-524.
22. P. Kang, and S. Cho, "EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems." ICONGP 2006, LNCS 4233, Part 1 Springer Hiedelberg, 2006, pp. 837-846