Intrusion detection using a hybrid support vector machine based on entropy and tf-idf

International Journal of Innovative Computing, Information and Control

Volumn 4, Issue 2, 2008, Pages 413-424

  Chen, Rung Ching ^a   Chen, Su Ping ^a  

^a CHAOYANG UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Entropy; Intrusion detection system; Support vector machine; TF IDF

Indexed keywords

EID: 63649137061     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. M. Botha and R.V. Solms, Utilising fuzzy logic and trend analysis for effective intrusion detection, Computers & Security, pp.423-434, 2003.
2. S. Mukkamala, Andrew H. Sung and A. Abraham, Intrusion detection using an ensemble of intelligent paradigms, Computer Application, pp.167-182, 2005.
3. D. E. Denning, An intrusion-detection model, IEEE Trans. Software Eng., pp.222-232, 1987.
4. T. Verwoerd and R. Hunt, Intrusion detection techniques and approaches, Computer Communications, pp.1356-1365, 2002.
5. M. Roesch, Snort-lightweight intrusion detection for network, Proc. of the LISA '99: 13th System Administration Conference, Seattle, Washington, pp.229-238, 1999.
6. S. Rubin, S. Jha and B. Miller, Automatic generation and analysis of NIDS attacks, Proc. of the 20th Annual Computer Security Application Conference, IEEE Computer Society, 2004.
7. W. H. Chen, S. H. Hsu and H. P. Shen, Application of SVM and ANN for intrusion detection, Computers & Operations Research, pp.2617-2634, 2005.
8. B. Balajinath and S. V. Raghavan, Intrusion detection through learning behavior model, Computer Communications, pp.1202-1212, 2001.
9. VN. Vapnik, The Nature of Statistical Learning Theory, New York: Springer, 1995.
10. Z. Zhang and H. Shen, Application of online-training SVMs for real-time intrusion detection with different considerations, Computer Communications, pp. 1428-1442, 2005.
11. H. Feng, O. Kolesnikov, P. Fogla, W. Lee and W. Gong, Anomaly detection using call stack information, Proc. of the IEEE Symposium on Security and Privacy, 2003.
12. A. S. Pandya and R. B. Macy, Pattern Recognition with Neural Networks in C++, IEEE Press, 1995.
13. Y. Liao and V. Rao Vemuri, Use of K-nearest neighbor classifier for intrusion detection, Computers & Security, pp.439-448, 2002.
14. T. Miyoshi and H. Joichi, Comparison with fuzzy reasoning and modified TF-IDF in page grouping for the result of web retrieval, International Journal of Innovative Computing, Information and Control, vol.3, no.2, pp.307-318, 2007.
15. G. Salton and C. Buckley, Term-weighting approaches in automatic text retrieval, Information Processing & Management, pp.513-523, 1988.
16. L. Feng, X. Guan, S. Guo, Y. Gao and P. Liu, Predicting the intrusion intentions by observing system call sequences, Computers & Security, pp.241-252, 2004.
17. D. Q. Naiman, Statistical anomaly detection via httpd data analysis, Computational Statistics & Data Analysis, pp.51-67, 2004.
18. D. Y. Yeung and Y. Ding, Host-based intrusion detection using dynamic and static behavioral models, Pattern Recognition, pp.229-243, 2003.
19. C. Shannon, Bell System Tech. J., 1948.
20. H. Y. Kao, S. H. Lin, J. M. Ho and M. S. Chen, Mining web informative structures and contents based on entropy Analysis, IEEE Trans, on Knowledge and Data Engineering, ρp.41-55, 2004.
21. O. Depren, M. Topallar, E. Anarim and M. Kemal Ciliz, An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks, Expert Systems with Applications, pp.713-722, 2005.
22. G. Giacinto, F. Roli and L. Didaci, Fusion of multiple classifiers for intrusion detection in computer networks, Pattern Recognition, pp.1795-1803, 2003.
23. R. C. Chen and C. H. Hsieh, Web page classification based on a support vector machine using a weighted vote schema, Expert System with Applications, vol.31, pp.427-435, 2006.
24. MIT, http://www.ll.mit.edu/IST/ideval/data/1998/1998.dataJndex.html
25. W. Lee and S. J. Stolfo, A framework for constructing features and models for intrusion detection systems, ACM Transactions on Information and System Security, vol.3, no.4, pp.227-261, 2003.
26. C.-C. Chang and C.-J. Lin, LIBSVM - A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/cjlin/libsvm
27. Website, http://163.17.27.154/dataset.
28. Guide of LIBSVM, http://www.csie.ntu.edu.tw/cjlin/papers/guide/guide.pdf
29. S. S. Keerthi and C. J. Lin, Asymptotic behaviors of support vector machines with Gaussian kernel, http://www.csie.ntu.edu.tw/cjlin/libsvm/faq. html#f404
30. Guide of LIBSVM, http://www.csie.ntu.edu.tw/cjlin/libsvm/faq.html