**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**30174

##### An Evolutionary Statistical Learning Theory

**Authors:**
Sung-Hae Jun,
Kyung-Whan Oh

**Abstract:**

**Keywords:**
Evolutionary computing,
Local optima,
Over-fitting,
Statistical learning theory

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1061609

**References:**

[1] A. Ben-Hur, A. D. Horn, H. Siegelmann, V. Vapnik, "Support Vector Clustering," Journal of Machine Learning Research 2, 2001, pp. 125-137.

[2] L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, Classification and Regression Trees, Wadsworth Inc., 1984.

[3] J. Cannady, "Artificial Neural Networks for Misuse Detection. National Information Systems," Proceedings of Security Conference, 1998.

[4] G. Casella, R. L. Berger, Statistical Inference, Duxbury Press, 1990.

[5] V. Cherkassky, F. Mulier, Learning From Data Concepts, Theory, and Methods, John Wiley & Sons, 1998.

[6] R. Cooley, B. Mobasher, J. Srivastava, "Web Mining: Information and Pattern Discovery on the World Wide Web," Proceeding of the 9th IEEE International Conference on Tools with Artificial Intelligence, 1997.

[7] R. Cooley, P. N. Tan, J. Srivastava, "Discovery of interesting usage patterns from web data," Technical Report TR 99-022, University of Minnesota, 1999.

[8] H. Debar, M. Becke, D. Siboni, "A Neural Network Component for an Intrusion Detection System," Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1992, pp. 240-250.

[9] H. Debar, B. Dorizzi, "An Application of a Recurrent Network to an Intrusion Detection System," Proceedings of the International Joint Conference on Neural Networks, 1992, pp 78-483.

[10] A. E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, Springer, 2003.

[11] S. M. Emran, M. Xu, N. Ye, Q. Chen, X. Li, "Probabilistic techniques for intrusion detection based on computer audit data," IEEE Transactions on Systems, Man and Cybernetics, Part A, vol.31, 2001, pp.266-274.

[12] D. Fisher, K. Hildrum, J. Hong, M. Newman, M. Thomas, R. Vuduc, "SWAMI: A Frame-work for Collaborative Filtering Algorithm Development and Evaluation," Proceeding of SIGIR 2000, ACM Press, 2000.

[13] D. B. Fogel, Evolutionary Computation, IEEE Press, 1995.

[14] L. J. Fogel, A. J. Owens, M. J. Walsh, Artificial Intelligence through Simulated Evolution, Wiley, Chichester, UK, 1996.

[15] A. K. Ghosh, Learning Program Behavior Profiles for Intrusion Detection, USENIX, 1999.

[16] J. W. Haines, R. P. Lippmann, D. J. Fried, M. A. Zissman, E. Tran, S. B. Boswell, "1999 DARPA Intrusion Detection Evaluation: Design and Procedures," Technical Report 1062, Lincoln Laboratory, MIT, 2001.

[17] S. Haykin, Neural Networks, Prentice Hall, 1999.

[18] S. Huet, A. Bouvier, M. A. Poursat, E. Jolivet, Statistical Tools for Nonlinear Regression, Springer Series in Statistics, Springer, 2003.

[19] S. H. Jun, "Hybrid Statistical Learning Model for Intrusion Detection of Networks," The KIPS Transaction: Part C, vol. 10-C, no. 6, 2003, pp. 705-710.

[20] S. H. Jun, "Web Usage Mining Using Support Vector Machine," Lecture Note in Computer Science, vol. 3512, 2005, pp. 349-356.

[21] S. Kumar, E. H. Spafford, "An Application of Pattern Matching in Intrusion Detection," Technical Report CSD-TR-94-013, Purdue University, 1994.

[22] W. Lee, S. J. Stolfo, K. W. Mok, "A data mining framework for building intrusion detection models," Proceedings of the 1999 IEEE Symposium on Security and Privacy, 1999, pp.120-132.

[23] R. J. A. Little, D. B. Rubin, Statistical Analysis with Missing Data, Wiley Inter-Science, 2002.

[24] B. Liu, "Fuzzy Random Chance-Constrained Programming," IEEE Transactions on Fuzzy Systems, vol. 9, Issue 5, 2001, pp. 713-720.

[25] J. Luo, S. M. Bridges, "Mining Fuzzy Association Rules and Fuzzy Frequency Episodes for Intrusion Detection," International Journal of Intelligent Systems, John Wiley & Sons, 2000, pp. 687-703.

[26] G. Mclachlan, D. Peel, Finite Mixture Models, John Wiley & Sons, Inc., 2000.

[27] T. M. Mitchell, Machine Learning, McGraw-Hill, 1997.

[28] T. M. Mitchell, An introduction to Genetic Algorithms, MIT Press, 1998.

[29] S. Mukkamala, G. Janoski, A. Sung, "Intrusion Detection Using Neural Networks and Support Vector Machines," Proceedings of International Symposium on Applications and the Internet Technology, 2000, pp. 209-216

[30] R. H. Myers, Classical and Modern Regression with Applications, Duxbury Press, 1990.

[31] A. T. Quang, Q. L. Zhang, X. Li, "Evolving Support Vector Machine Parameters," Proceedings of the First International Conference on Machine Learning and Cybernetics, 2002, pp. 548-551.

[32] J. Ryan, M. J. Lin, R. Miikkulainen, "Intrusion Detection with Neural Networks," Advances in Neural Information Processing Systems 10, Cambridge, MA: MIT Press, 1998.

[33] A. J. Smola, Regression estimation with support vector learning machines, Master-s thesis, Technische University, 1996.

[34] V. Vapnik, Statistical Learning Theory, John Wiley & Sons, Inc., 1998.

[35] X. Yao, "Evolving Artificial Neural Networks," Proceedings of the IEEE, vol. 87, Issue 9, 1999, pp. 1423-1447.

[36] http://www.ecn.purdue.edu/KDDCUP

[37] http://www.ll.mit.edu/IST/ideval/data