Evolutionary Optimization of Radial Basis Function Networks for Intrusion Detection

Proceedings of the International Joint Conference on Neural Networks

Volumn 1, Issue , 2003, Pages 415-420

  Hofmann, Alexander ^a   Sick, Bernhard ^a  

^a UNIVERSITY OF PASSAU   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER ARCHITECTURE; COMPUTER CRIME; EVOLUTIONARY ALGORITHMS; FEATURE EXTRACTION; FUZZY CONTROL; MULTILAYER NEURAL NETWORKS; STRUCTURAL OPTIMIZATION;

NEURONS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 0141593896     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (47)

1. C. M. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
2. S. Haykin, Neural Networks - A Comprehensive Foundation, Macmillan College Publishing Company, New York, 1994.
3. T. Poggio and F. Girosi, "A theory of networks for approximation and learning," A.I. Memo No. 1140, C.B.I.P. Paper No. 31, Massachusetts Institute of Technology - Artificial Intelligence Laboratory & Center for Biological Information Procesing - Whitaker College, 1989.
4. T. F. Lunt, "A survey of intrusion detection techniques," Computers and Security, vol. 12, no. 4, pp. 405 - 418, 1993.
5. A. Wespi, G. Vigna, and L. Deri, Eds., Recent Advances in Intrusion Detection, Number 2516 in LNCS. Springer Verlag, Berlin, Heidelberg, New York, 2002, (Proc. of the 5th Int. Symp. RAID 2002, Zurich).
6. D. J. Marchette, Computer Intrusion Detection and Network Monitoring - A Statistical Viewpoint, Springer-Verlag, New York, Berlin, Heidelberg, 2001.
7. S. Northcutt and J. Novak, Network Intrusion Detection, New Riders, Indianapolis, 3rd edition, 2002.
8. A. Hofmann, C. Schmitz, and B. Sick, "Intrusion detection in computer networks with neural and fuzzy classifiers," in Proc. of the 12th Int. Conf. on Artificial Neural Networks (ICANN 2003), Istanbul, 2003, (to appear).
9. H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining, Kluwer Academic Publishers, Boston, 1998.
10. H. Liu and H. Motoda, Eds., Feature Extraction, Construction, and Selection: A Data Mining Perspective, Kluwer Academic Publishers, Boston, 1998.
11. B. Sick, "Technische Anwendungen von Soft-Computing Methoden," Lecture Notes, University of Passau, July 2001.
12. R. Reed, "Pruning algorithms - a survey," IEEE Tr. on Neural Networks, vol. 4, no. 5, pp. 740 - 747, 1993.
13. J. D. Schaffer, D. Whitley, and L. J. Eshelman, "Combinations of genetic algorithms and neural networks: A survey of the state of the art," in Int. Workshop on Combinations of Genetic Algorithms and Neural Networks (COGANN-92), Los Alamitos, 1992, pp. 1 - 37.
14. X. Yao, "Evolving artificial neural networks," Proc. of the IEEE, vol. 87, no. 9, pp. 1423 - 1447, 1999.
15. E. Lacerda, A. de Carvalho, and T. Ludermir, "Evolutionary optimization of RBF networks," in Radial Basis Function Networks 1 - Recent Developments in Theory and Applications, R. J. Howlett and L. C. Jain, Eds. Physica Verlag, Heidelberg, New York, 2001.
16. M. W. Mak and K. W. Cho, "Genetic evolution of radial basis function centers for pattern classification," in Int. Joint Conf. on Neural Networks (IJCNN '98), Anchorage, 1998, vol. 1, pp. 669 - 673.
17. B. A. Whitehead and T. D. Choate, "Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction," IEEE Tr. on Neural Networks, vol. 7, no. 4, pp. 869 - 880, 1996.
18. B. A. Whitehead and T. D. Choate, "Evolving space-filling curves to distribute radial basis functions over an input space," IEEE Tr. on Neural Networks, vol. 5, no. 1, pp. 15 - 23, 1994.
19. S. A. Billings and G. L. Zheng, "Radial basis function network configuration using genetic algorithms," Neural Networks, vol. 8, no. 6, pp. 877 - 890, 1998.
20. H. Leung, N. Dubash, and N. Xie, "Detection of small objects in clutter using a GA-RBF neural network," IEEE Tr. on Aerospace and Electronic Systems, vol. 38, no. 1, pp. 98 - 118, 2002.
21. W. Zhao, D.-S. Huang, and G. Yunjian, "The structure optimization of radial basis probabilistic neural networks based on genetic algorithms, " in Proc. of the 2002 Int. Joint Conf. on Neural Networks (IJCNN '02), Honolulu, 2002, vol. 2, pp. 1086 - 1091.
22. X. Fu and L. Wang, "A GA-based RBF classifier with class-dependent features," in Proc. of the 2002 Congr. on Evolutionary Comp. (CEC '02), Honolulu, 2002, vol. 2, pp. 1890 - 1894.
23. X. Fu and L. Wang, "Rule extraction from an RBF classifier based on class-dependent features," in Proc. of the 2002 Congr. on Evolutionary Comp. (CEC '02), Honolulu, 2002, vol. 2, pp. 1916 - 1921.
24. S. Axelsson, "Intrusion detection systems: A survey and taxonomy," Tech. Rep. 99-15, Department of Computer Engineering, Chalmers University of Technology, Göteborg, 2000.
25. S. Axelsson, "Research in intrusion detection systems: A survey," Tech. Rep. 98-17, Department of Computer Engineering, Chalmers University of Technology, Göteborg, 1999, (revised version).
26. R. P. Lippmann and R. K. Cunningham, "Improving intrusion detection performance using keyword selection and neural networks," Computer Networks, vol. 34, no. 4, pp. 597 - 603, 2000.
27. Z. Liu, G. Florez, and S. M. Bridges, "A comparison of input representations in neural networks: a case study in intrusion detection," in Proc. of the Int. Joint Conf. on Neural Networks (IJCNN 2002), Honolulu, 2002, vol. 2, pp. 1708 - 1713.
28. S. C. Lee and D. V. Heinbuch, "Training a neural-network based intrusion detector to recognize novel attacks," IEEE Tr. on Systems, Man and Cybernetics, Part A, vol. 31, no. 4, pp. 294 - 299, 2001.
29. S. Mukkamala, G. Janoski, and A. Sung, "Intrusion detection using neural networks and support vector machines," in Proc. of the Int. Joint Conf. on Neural Networks (IJCNN 2002), Honolulu, 2002, vol. 2, pp. 1702 - 1707.
30. J. Ryan, M.-J. Lin, and R. Miikkulainen, "Intrusion detection with neural networks," in Advances in Neural Information Processing Systems 10 (NIPS '97), M. Jordan, M. Kearns, and S. Solla, Eds., pp. 943 - 949. MIT Press, Cambridge, 1998.
31. L. Wang, G. Yu, G. Wang, and D. Wang, "Method of evolutionary neural network-based intrusion detection," in Int. Conf. on Info-tech and Info-net (ICH 2001), Beijing, Y. X. Zhong, S. Cui, and Y. Wang, Eds., 2001, vol. 5, pp. 13 - 18.
32. H. Debar and B. Dorizzi, "An application of a recurrent network to an intrusion detection," in Proc. of the Int. Joint Conf. on Neural Networks (IJCNN 1992), Baltimore, 1992, vol. 2, pp. 478 - 483.
33. J. Cannady, "Applying CMAC-based online learning to intrusion detection," in Proc. of the IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks (IJCNN 2000), Como, 2000, vol. 5, pp. 405 - 410.
34. J. Cannady and R. C. Garcia, "The application of fuzzy ARTMAP in the detection of computer network attacks," in Artificial Neural Networks ICANN 2001 (Proc. of the 11th Int. Conf. on Artificial Neural Networks), Vienna, G. Dorffner, H. Bischof, and K. Homik, Eds. 2001, number 2130 in LNCS, pp. 225 - 230, Springer Verlag, Berlin, Heidelberg, New York.
35. A. J. Hoglund, K. Hatonen, and A. S. Sorvari, "A computer host-based user anomaly detection system using the self-organizing map," in Proc. of the IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks (IJCNN 2000), Como, 2000, vol. 5, pp. 411 - 416.
36. B. C. Rhodes, J. A. Mahaffey, and J. D. Cannady, "Multiple self-organizing maps for intrusion detection," in Proc. of the 23rd National Information Systems Security Conf. (NISSC 2000), Baltimore, 2000.
37. D. Dasgupta and H. Brian, "Mobile security agents for network traffic analysis," in Proc. of DARPA Information Survivability Conference & Exposition II (DISCEX '01), Anaheim, 2001, vol. 2, pp. 332 - 340.
38. R. Durst, T. Champion, B. Witten, E. Miller, and L. Spagnuolo, "Testing and evaluating computer intrusion detection systems," Communications of the ACM, vol. 42, pp. 53 - 61, 1999.
39. R. P. Lippmann, J. W. Haines, D. J. Fried, J. Korba, and K. Das, "The 1999 DARPA off-line intrusion detection evaluation," Computer Networks, vol. 34, no. 4, pp. 579 - 595, 2000.
40. R. P. Lippmann, D. J. Fried, I. Graf, J. W. Haines, K. R. Kendall, D. McClung, D. Weber, S. E. Webster, D. Wyschogrod, R. K. Cunningham, and M. A. Zissman, "Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation," in Proc. of the 2000 DARPA Information Survivability Conf. and Exposition (DISCEX), Hilton Head. 1999, vol. 2, pp. 12 - 26, IEEE Press.
41. Y. Jin, W. von Seelen, and B. Sendhoff, "Extracting interpretable fuzzy rules from RBF neural networks," Int. Rep. 2000-02, Inst, für Neuroinformatik (INF), Ruhr-Universität Bochum, 2000.
42. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin, Heidelberg, New York, 3rd edition, 1996.
43. T. Bäck, Evolutionary Algorithms in Theory and Practice, Ph.D. thesis, Universität Dortmund, Fachbereich Informatik, 1994.
44. S. G. Roberts and M. Turega, "Evolving neural network structures: An evaluation of encoding techniques," in Artificial Neural Nets and Genetic Algorithms, Alès, D. W. Pearson, N. C. Steele, and R. F. Albrecht, Eds. 1995, pp. 96 - 99, Springer Verlag, Wien, New York.
45. H. Pohlheim, Evolutionäre Algorithmen: Verfahren, Operatoren und Hinweise für die Praxis, Springer-Verlag, Berlin, 2000.
46. A. Hofmann, "Einsatz von Soft-Computing-Verfahren zur Erkennung von Angriffen auf Rechnemetze," M.S. thesis, University of Passau, 2002.
47. V. Nissen, Einführung in Evolutionäre Algorithmen, Friedrich Vieweg & Sohn Verlagsgesellschaft, Braunschweig, Wiesbaden, 1997.