An anomaly intrusion detection system based on vector quantization

IEICE Transactions on Information and Systems

Volumn E89-D, Issue 1, 2006, Pages 201-230

  Zheng, Jun ^a   Hu, Mingzeng ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Anomaly intrusion detection; Codebook; Quantization error; Usage profile; Vector quantization

Indexed keywords

ALGORITHMS; CONGESTION CONTROL (COMMUNICATION); DATA MINING; LEARNING SYSTEMS; SECURITY SYSTEMS; TELECOMMUNICATION TRAFFIC;

ANOMALY INTRUSION DETECTION (AID); CODEBOOK; QUANTIZATION ERRORS; USAGE PROFILE;

VECTOR QUANTIZATION;

EID: 32244436344     PISSN: 09168532     EISSN: 17451361     Source Type: Journal    
DOI: 10.1093/ietisy/e89-d.1.201     Document Type: Conference Paper

References (35)

1. T. Verwoerd and R. Hunt, "Intrusion detection techniques and approaches," Comput. Commun., vol.25, no.15, pp.1356-1365, Sept. 2002.
2. J.M. Estvez-Tapiador, P. Garcia-Teodoro, and J.E. Diaz-Verdejo, "Anomaly detection methods in wired networks: A survey and taxonomy," Comput. Commun., vol.27, no. 16, pp. 1569-1584, Oct. 2004.
3. A. Gersho and R.M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers, Boston, 1992.
4. http://www.snort.org,2004
5. D.E. Denning. "An intrusion-detection model," IEEE Trans. Softw. Eng., vol.13, no.2, pp.222-232, Feb. 1987.
6. N. Ye, "Multivariate statistical analysis of audit trails for host-based intrusion detection," IEEE Trans. Comput., vol.51, no.13, pp.810-820, Jan. 2002.
7. S.M. Emran and N. Ye, "Robustness of chi-square and Canberra techniques in detecting intrusions into information systems," Qual. Reliab. Eng. Int., vol.18, no.1, pp.19-28, Jan. 2002.
8. E. Eskin, A. Arnold, and M. Prerau, "A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled-data," in Applications of Data Mining in Computer Security, Kluwer, 2002.
9. S.H. Oh and W.S. Lee, "A clustering-based anomaly intrusion detection for a host computer," IEICE Trans. Inf. & Syst., vol.E87-D, no.8, pp.2086-2094, Aug. 2004.
10. L. Portnoy, E. Eskin, and S.J. Stolfo, "Intrusion detection with unlabeled data using clustering," Proc. ACM CSS Workshop on Data Mining Applied to Security, pp. 123-130, Philadelphia, PA, June 2001.
11. E. Eskin, "Anomaly detection over noisy data using learned probability distributions," Proc. 17th Int. Conf. Machine Learning, pp.255-262, San Francisco, CA, July 2000.
12. E. Knorr and R. Ng, "Algorithms for mining distance-based outliers in large datasets," Proc. VLDB Conference, pp.392-403, New York, USA, Sept. 1998.
13. A. Lazarevic, L. Ertoz, A. Ozgur, and J. Srivastava, "A comparative study of anomaly detection schemes in network intrusion detection," Proc. SIAM Conf. Data Mining, pp.108-120, Orlando, FL, May 2003.
14. S. Mukkamala, G. Janowski, and AH. Sung, "Intrusion detection using neural networks and support vector machines," Proc. Hybrid Information Systems Advances in Soft Computing, pp. 121-138, Physica/Springer, Heidelberg, Dec. 2001.
15. A.H. Sung and S. Mukkamala, "Identifying important features for intrusion detection using support vector machines and neural networks," Proc. 2003 Symposium on Applications and the Internet, pp.209-217, Orlando, USA, Jan. 2003.
16. Y. Qiao, X.W. Xin, Y. Bin, and S. Ge, "Anomaly intrusion detection method based on HMM," Electron. Lett., vol.38, no. 13, pp.663-664, June 2002.
17. J.M. Bonifaco and E.S. Moreira, "An adaptive intrusion detection system using neural network," Research Report, UNESP, Brazil, 1997.
18. C. Warrender, S. Forrest, and B. Pearlmutter, "Detecting intrusions using system calls: Alternative data models," Proc. 1999 IEEE Symposium on Security and Privacy, pp. 133-145, Oakland, USA, May 1999.
19. S. Mukkamala, G.I. Janoski, and A.H. Sung, "Intrusion detection using support vector machines," Proc. High Performance Computing Symposium - HPC 2002, pp. 178-183, San Diego, USA, April 2002.
20. L. Wenke, S.J. Stolfo, and K.W. Mok, "A data mining framework for building intrusion detection models," Proc. 1999 IEEE Symposium on Security and Privacy, pp.296-304, Oakland, USA, May 1999.
21. Y. Liao and V. Rao Vemuri, "Use of k-nearest neighbor classifier for intrusion detection," Comput. Secur., vol.21, no.5, pp.439-448, Oct. 2002.
22. L.P. Cordella, A. Limongiello, and C. Sansone, "Network intrusion detection by a multi-stage classification system," Proc. Multiple Classifier Systems: 5th International Workshop, MCS 2004, pp.324-333, Cagliari, Italy, June 2004.
23. M. Ramadas, S. Ostermann, and B. Tjaden, "Detecting anomalous network traffic with self-organizing maps," Proc. Recent Advances in Intrusion Detection: 6th International Symposium, RAID 2003, pp.34-56, Pittsburgh, PA, USA, Sept. 2003.
24. J.Z. Lei and A. Ghorbani, "Network intrusion detection using an improved competitive learning neural network," Proc. Second Annual Conference on Communication Networks and Services Research (CNSR'04), pp. 190-197, Fredericton, N.B., Canada, May 2004.
25. Y. Linde, A. Buzo, and R.M. Gray, "An algorithm for vector quantizer design," IEEE Trans. Commun., vol.28, no.1, pp.84-95, Jan. 1980.
26. K. Goh and E. Chang, "Indexing multimedia data in high-dimensional and weighted feature spaces," Proc. 6th Visual Database Conference, pp. 127-140, Brisbane, Australia, May 2002.
27. H. Ferhatosmanoglu, E. Tuncel, D. Agrawal, and A. El Abbadi, "Approximate nearest neighbor searching in multimedia databases," Proc. 17th IEEE Int. Conf. on Data Engineering, pp.503-511, Heidelberg, Germany, April 2001.
28. H. Teng, K. Chen, and S. Lu, "Adaptive, real-time anomaly detection using inductively generated sequential patterns," Proc. IEEE Symposium on Research in Security and Privacy, pp.278-284, Oakland, USA, May 1990.
29. N. Ueda and R. Nakano, "A new competitive learning approach based on an equidistoition principle for designing optimal vector quantizers," IEEE Trans. Neural Netw., vol.7, no.8, pp. 1211-1227, Sept. 1994.
30. T. Kohonen, Self-Organization Maps, 3rd ed., Springer-Verlag, Berlin, 2000.
31. Z. Lu, J. Pan, and S. Sun, "A fast codebook search algorithm for vector quantization," Acta Electronica Sinica, vol.28, no.2, pp. 133-135, Feb. 2000.
32. http://libnids.sourceforge.net/
33. http://www.ll.mit.edu/IST/ideval/index.html
34. R. Lippmann, J.W. Haines, D.J. Fried, J. Korba, and K. Das, "The 1999 DARPA off-Line intrusion detection evaluation," Comput. Netw., vol.34, no.4, pp.579-595, Oct. 2000.
35. S. Mukkamala, A.H. Sung, and A. Abraham, "Intrusion detection using an ensemble of intelligent paradigms," J. Netw. Comput. Appl., vol.28, no.2, pp.167-182, Feb. 2005.