Integration of Self-Organizing Map (SOM) and Kernel Density Estimation (KDE) for network intrusion detection

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7480, Issue , 2009, Pages

  Cao, Yuan ^a   He, Haibo ^a   Man, Hong ^a   Shen, Xiaoping ^b  

^a Stevens Institute of Technology   (United States)

^b OHIO UNIVERSITY   (United States)

Author keywords

Anomaly based network intrusion detection (ABNID); Kernel density estimation (KDE); Machine learning; Self organizing map (SOM)

Indexed keywords

ABNORMAL BEHAVIOR; COMPUTATIONAL COSTS; CYBER SYSTEMS; DATA CLUSTERS; INFORMATION SECURITY; INPUT PATTERNS; INPUT SPACE; INTEGRATED NETWORKS; KERNEL DENSITY ESTIMATION; KERNEL DENSITY ESTIMATORS; LEARNING CAPABILITIES; MACHINE-LEARNING; NET-CENTRIC; NETWORK INTRUSION DETECTION; NETWORK SYSTEMS; NETWORK TECHNOLOGIES; NETWORK TRAFFIC; OHIO UNIVERSITY; REAL WORLD DATA; REAL-TIME PROTECTION; SELF-ORGANIZE;

ALGORITHMS; COMPUTATIONAL EFFICIENCY; CONFORMAL MAPPING; COST REDUCTION; EDUCATION; ESTIMATION; INTERNET; INTRUSION DETECTION; RANDOM PROCESSES; RANDOM VARIABLES; ROBOT LEARNING; STRENGTH OF MATERIALS;

SENSOR NETWORKS;

EID: 71549156238     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.834890     Document Type: Conference Paper

References (31)

1. S. Smaha, "Haystack: An Intrusion Detection System," in the Fourth Aerospace Computer Security Applications Conference, pp. 37 - 44, Orlando, FL, 1988.
2. th National Computer Security Conference, pp 74-81, 1988.
3. D. Anderson, T. Frivold, and A. Valdes, "Next-Generation Intrusion Detection Expert System," Technical Report, SRI International, 1995.
4. G. White and V. Pooch, "Cooperating Security Managers: Distributed Intrusion Detection Systems," Computers & Security, vol. 15, no. 5, pp. 441-450, 1996.
5. T. Heberlein, G. Dias, K. Levitt, B. Mukherjee, J. Wood, and D. Wolber, "A Network Security Monitor," in Proceedings of the 1990 IEEE symposium on Research in Security and Privacy, pp. 296, 1990.
6. SNORT: The Open Source Intrusion Detection System. [Available] URL: http://www.snort.org.
7. th National Information Systems Security Conference, pp. 1-16, 1997.
8. V. Barnett and T. Lewis, Outliers in Statistical Data, New York, NY, John Wiley and Sons, 1994.
9. C. C. Aggarwal and P. Yu, "Outlier detection for high dimensional data," in Proceedings of the ACM SIGMOD International Conference on Management of Data, 2001.
10. S. Kumar and E. Spafford. An Application of Pattern Matching in Intrusion Detection. Technical Report 94-013, Purdue University, Department of Computer Sciences, Mar. 1994.
11. S. Hawkins, H. He, G. Williams and R. Baxter, Outlier Detection Using Replicator Neural Networks, In Proc. of the 4th Int. Conf. on Data Warehousing and Knowledge Discovery (DaWaK02), Aix-en-Provence, France, pp. 170-180, 2002.
12. K. Narita and H. Kitagawa, "Outlier Detection for Transaction Databases Using Association Rules," in the Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management, vol. 00, pp. 373-380 , 2008.
13. th edition. New York: W. H. Freeman, 2002.
14. st edition, Chapman & Hall/CRC, 1986.
15. J. D. F. Habbema, J. Hermans, and K. van der Broek, "A Stepwise Discrimination Program Using Density Estimation," In G. Bruckman, editor, Compstat, pp. 100-110. Vieanna: Physica Verlag, 1974.
16. A. W. Bowman, "An Alternative Method of Cross-Validation for the Smoothing of Density Esitmation," Biometrika, vol. 71, no. 2, pp. 353-360, 1984.
17. D. W. Scott and G. R. Terrell, "Biased and Unbiased Cross-validation in Density Estimation," J. Amer. Statist. Assoc., vol. 82, no. 400, pp. 1131-1146, December 1987.
18. P. Hall, S. J. Sheather, M. C. Jones, and J. S. Marron, "On Optimal Data-based Bandwidth Selection in Kernel Density Estimation," Biometrika, vol. 78, no. 2, pp. 263-269, 1991.
19. B. U. Park and J. S. Marron, "Comparison of Data-driven Bandwidth Selectors," J. Amer. Statist. Assoc., vol. 85, no. 409, pp. 66-72, 1990.
20. M. C. Jones and R. F. Kappenman, "On a class of kernel density estimate bandwidth selectors," Scand. J. Statist., vol. 19, pp. 337-349, 1991.
21. M. C. Jones, J. S. Marron, S. J. Sheather, "A Brief Survey of Bandwidth Selection for Density Estimation," J. of the American Statistical Association, vol. 91, 1996.
22. V. C. Raykar and R. Duraiswami. Fast optimal bandwidth selection for kernel density estimation. Proceedings of the sixth SIAM International Conference on Data Mining, pages 524-528, 2006.
23. D. W. Scott and S. R. Rain. Multi-dimensional density estimation. Data Mining and Computational Statistics, 23, 2004.
24. T. Kohonen, "The Self-organizing map,", Neurocomputing, Elsevier, 1998.
25. T. Kohonen, Self-organizing Maps, Springer, 2001.
26. T. Kohonen, "The Self-organizing Map," Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, September 1990.
27. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd edition, pp. 446-454, Prentice Hall, Upper Saddle River, New Jersey 07458, 1999.
28. R. Balupari, Real-Time Network-Based Anomaly Intrusion Detection, Master's thesis, Ohio University, 2002.
29. A. Hämäläinen, Self-organizing Map and Reduced Kernel Density Estimation, PhD thesis, University of Jyväskylä, Jyväskylä, Finland, 1995.
30. H. Yin and N. M. Allinson, "Self-organizing mixture networks for probability density estimation", IEEE Trans. Neural Networks, vol. 12, no. 2, pp. 405-411, 2001.
31. A. Elgammal, R. Duraiswami, and L. Davis, "The Fast Gauss Transform for efficient kernel density evaluation with applications in computer vision," IEEE Trans. PAMI., vol. 25, pp. 1499- 1504, 2003.