Computational intelligent techniques for detecting denial of service attacks

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3029, Issue , 2004, Pages 616-624

  Mukkamala, Srinivas ^a   Sung, Andrew H ^a  

^a NEW MEXICO INSTITUTE OF MINING AND TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; COMPUTATIONAL METHODS; COMPUTER SIMULATION; DATA ACQUISITION; INFORMATION RETRIEVAL; INTERNET; MATHEMATICAL MODELS; REGRESSION ANALYSIS; USER INTERFACES; ARTIFICIAL INTELLIGENCE; COMPUTER CRIME; CRIME; EXPERT SYSTEMS; INFORMATION SCIENCE; INTELLIGENT AGENTS; NETWORK SECURITY; SUPPORT VECTOR MACHINES; TRANSMISSION CONTROL PROTOCOL;

DENIAL OF SERVICE ATTACKS (DOS); LINEAR GENETIC PROGRAMS (LGP); MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS); SUPPORT VECTOR MACHINES (SVM); COMPUTATIONAL INTELLIGENT TECHNIQUES; DISTRIBUTED DENIAL OF SERVICE ATTACK; INFORMATION ASSURANCE; INFORMATION RESOURCE; LIMITED INFORMATION; MULTIPLICATIVE EFFECTS; MULTIVARIATE ADAPTIVE REGRESSION SPLINES; SUPPORT VECTOR MACHINE (SVMS);

INTELLIGENT STRUCTURES; DENIAL-OF-SERVICE ATTACK;

EID: 9444289934     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-24677-0_63     Document Type: Conference Paper

References (21)

1. T. Draelos, et. AI, "Distributed Denial of Service Characterization," Technical Report, Sandia National Laboratories, 2003.
2. C. Shields, "What do we mean by network denial of service?" Proceedings of the 2002 IEEE workshop on Information Assurance. US Military Academy, 2002, pp. 196-203.
3. W. J. Blackert, D. C. Furnanage, and Y. A. Koukoulas, "Analysis of Denial of service attacks Using An address Resolution Protocol Attack, " Proceedings of the 2002 IEEE Workshop on Information Assurance. US Military Academy, 2002, pp. 17-22.
4. D. W. Gresty, Q. Shi, and M. Merabti, "Requirements for a general framework for response to distributed denial of service," Seventeenth Annual Computer Security Applications Conference, 2001, pp. 422-229.
5. J. Mirkovic, J. Martin, and P. Reiher, "A Taxonomy of DDoS Attacks and DDoS Defense Mechanisms," Technical Report # 020017, Department of Computer Science, UCLA, 2002.
6. K. Park, and H. Lee, "On the Effectiveness of Router-Based Packet Filtering for Distributed DoS attack and Prevention in Power-Law Internets," Proceedings of the SGICOMM'01, 2001, pp. 15-26.
7. S. Mohiuddin, S. Hershkop, R. Bhan, S. Stolfo, "Defeating Against Large Scale Denial of Service Attack," Proceedings of the 2002 IEEE Workshop on Information Assurance. US Military Academy, 2002, pp. 139-146.
8. S. Mukkamala, and A. H. Sung, "Identifying Significant Features for Network Forensic Analysis Using Artificial Intelligence Techniques," In International Journal on Digital Evidence, IJDE Volume 1, Issue 4.
9. S. Mukkamala, and A. H. Sung, "Detecting Denial of Service Attacks Using Support Vector Machines," Proceedings of IEEE International Conference on Fuzzy Systems, IEEE Computer Society Press, 2003, pp. 1231-1236.
10. K. Kendall, "A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems," Master's Thesis, Massachusetts Institute of Technology, 1998.
11. S. E. Webster, "The Development and Analysis of Intrusion Detection Algorithms," S.M. Thesis, Massachusetts Institute of Technology, 1998.
12. W. Lee, S. J. Stolfo, and K. Mok, "Mining Audit Data to Build Intrusion Detection Models," Proceedings of the KDD-98, honorable mention best application paper, 1998.
13. T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently," Proceedings of the International Conference on Machine Learning, Morgan Kaufman, 2000.
14. T. Joachims, "SVMlight is an Implementation of Support Vector Machines (SVMs) in C," http://ais.gmd.de/~thorsten/svm_light. University of Dortmund. Collaborative Research Center on Complexity Reduction in Multivariate Data (SFB475), 2000.
15. V. M. Vladimir, "The Nature of Statistical Learning Theory," Springer, 1995.
16. J. H. Friedman, Multivariate Adaptive Regression Splines, Annals of Statistics, Vol 19, 1991, pp. 1-141.
17. D. Steinberg, P. L. Colla, and K. Martin, "MARS User Guide," San Diego, CA: Salford Systems, 1999.
18. W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone, "Genetic Programming: An Introduction on the Automatic Evolution of Computer Programs and its Applications," Morgan Kaufmann Publishers, Inc, 1998.
19. AIMLeaming Technology, http://www.aimleaming.com.
20. S. Mukkamala, and A. H. Sung, "Feature Selection for Intrusion Detection Using Neural Networks and Support Vector Machines," To appear in Journal of the Transportation Research Board (of the National Academies).
21. S. Mukkamala, G. Janoski, and A. H. Sung, "Intrusion Detection Using Neural Networks and Support Vector Machines," Proceedings of IEEE International Joint Conference on Neural Networks, 2002, pp.1702-1707.