A denial of service detector based on maximum likelihood detection and the random neural network

Computer Journal

Volumn 50, Issue 6, 2007, Pages 717-727

  Öke, Gülay ^a   Loukas, Georgios ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Denial of service; Intrusion detection; Maximum likelihood detection criterion; Network security; Random neural networks

Indexed keywords

DETECTORS; MAXIMUM LIKELIHOOD ESTIMATION; NETWORK SECURITY; NEURAL NETWORKS; STATISTICS; TELECOMMUNICATION TRAFFIC;

DENIAL OF SERVICE DETECTION; RANDOM NEURAL NETWORK;

INTRUSION DETECTION;

EID: 35648969617     PISSN: 00104620     EISSN: 14602067     Source Type: Journal    
DOI: 10.1093/comjnl/bxm066     Document Type: Article

References (33)

1. SecurityFocus, August 2004: FBI busts alleged DDoS Mafia. http://www.securityfocus.com/news/9411.
2. BBC, September 2001: Teenager cleared of hacking. http://news.bbc.co.uk/ l/hi/england/hampshire/dorset/3197446.stm.
3. WiredNews: Pentagon Hacker Exposed by Just. Dpt. http://www.wired.com/ news/technology/0,1282,11030,00.html.
4. Jalili, R., Imani-Mehr, F., Amini, M. and Shahriari, H.R. (2005) Detection of Distributed Denial of Service Attacks Using Statistical Pre-processor and Unsupervised Neural Networks. Lecture Notes in Computer Science, vol. 3439, pp. 192-203.
5. Kim, M., Na, H., Chae, K., Bang, H. and Na, J (2004) A Combined Data Mining Approach for DDoS Attack Detection. Lecture Notes in Computer Science, vol. 3090, pp. 943-950.
6. Gavrilis, D. and Dermatas, E. (2005) Real-time detection of distributed denial-of-service attacks using RBF networks and statistical features. Comput. Netw., 48, 235-245.
7. He, H.T., Luo, X.N. and Liu, B.L. (2005) Detecting Anomalous Network Traffic with Combined Fuzzy-Based Approaches. Lecture Notes in Computer Science, vol. 3645, pp. 433-442.
8. Wei, W., Dong, Y., Lu, D. and Jin, G. (2006) Combining Cross-Correlation and Fuzzy Classification to Detect Distributed Denial-of-Service Attacks. Lecture Notes in Computer Science, vol. 3994, pp. 57-64.
9. Mukkamala, S. and Sung, A.H. (2004) Computational Intelligent Techniques for Detecting Denial of Service Attacks. Lecture Notes in Artificial Intelligence, vol. 3029, pp. 616-624.
10. Sung, A.H. and Mukkamala, S. (2004) The Feature Selection and Intrusion Detection Problems. Lecture Notes in Computer Science, vol. 3321, pp. 468-482.
11. Li, M. (2006) Change trend of averaged burst parameter of traffic under DDOS flood attacks. Comput. Secur., 25, 213-220.
12. Xiang, Y., Lin, Y., Lei, W.L. and Huang, S.J. (2004) Detecting DDOS attack based on Network Self-Similarity. IEE Proceedings in Communication, vol. 151, pp. 292-295.
13. Li, M. (2004) An approach to reliably identifying signs of DDOS flood attacks based on LRD traffic pattern recognition. Comput. Secur., 23, 549-558.
14. Feinstein, L., Schnackenberg, D., Balupari, R. and Kindred, D. (2003) Statistical approaches to DDoS attack detection and response. Proceedings of the DARPA Information Survivability Conference and Exposition (DISCEX'03).
15. Kulkarni, A.B., Bush, S.F. and Evans, S. (2002) Detecting distributed denial-of-service attacks using kolmogorov complexity metrics. Technical Information Series 2001CRD176. General Electric Company.
16. Furuya, T., Matsuzaki, T. and Matsuura, K. (2005) Detection of Unknown DoS Attacks by Kolmogorov-Complexity Fluctuation. Lecture Notes in Computer Science, vol. 3822, pp. 395-406.
17. Yang, X., Liu, Y., Zeng, M. and Shi, Y. (2004) A Novel DDoS Attack Detecting Algorithm Based on the Continuous Wavelet Transform. Lecture Notes in Computer Science, vol. 3309, pp. 173-181.
18. Li, L. and Lee, G. (2005) DDoS attack detection and wavelets. Telecommun. Syst., 28, 435-451.
19. Noh, S., Lee, C., Choi, K. and Jung, G. (2003) Detecting Distributed Denial of Service (DDoS) Attacks through Inductive Learning. Lecture Notes in Computer Science, vol. 2690, pp. 286-295.
20. Chen, Y. and Hwang, K. (2006) Collaborative detection and filtering of shrew DDoS attacks using spectral analysis. Parallel Distrib. Comput. 66, 1137-1151.
21. Gelenbe, E. (1993) Learning in the recurrent random neural network. Neural Comput., 5, 154-164.
22. Gelenbe, E., Lent, R. and Nunez, A. (2004) Self-aware networks and QoS. Proc. IEEE, 92, 1478-1489.
23. Duda, R.O., Hart, P.E. and Stork, D.G. (2001) Pattern Classification pp. 20-214. John-Wiley and Sons.
24. Shannon, C.E. and Weaver, W. (1963) The Mathematical Theory of Communication. University of Illinois Press.
25. Cajueiro, D.O. and Tabak, B.M. (2004) The Hurst exponent over time:tEsting the assertion that emerging markets are becoming more efficient. Physica A, 336, 521-537.
26. Cramer, C., Gelenbe, E. and Bakircioglu, H. (1996) Low bit-rate video compression with neural networks and temporal subsampling. Proc. IEEE 84, 1529-1543.
27. Teke, A. and Atalay, V. (2006) Texture classification and retrieval using the random neural network model. Comput. Manage. Sci., 3 193-205.
28. Gelenbe, E., Koubi, V. and Pekergin, F. (1993) Dynamical random neural network approach to the traveling salesman problem. In: Systems, Man and Cybernetics, 1993. 'Systems Engineering in the Service of Humans', IEEE Conference Proceedings, 2, October 17-20 1993, pp. 630-635 (ISBN: 0-7803-0911-1).
29. Abdelbaki, H.: Matlab simulator for the RNN. http://www/cs/ucf.edu/ ahossam/rnsim.
30. UCLA CSD packet traces. http://www.lasr.cs.ucla.edu/ddos/traces/public/ usc/.
31. Morein, W.G., Stavrou, A., Cook, D.L., Keromytis, A.D., Mishra, V. and Rubenstein, D. (2003) Using graphic Turing tests to counter automated DDoS attacks against Web servers. Proc. 10th ACM Int'l Conference on Computer and Communications Security (CCS '03), pp. 8-19.
32. Gelenbe, E., Gellman, M. and Loukas, G. (2005) An autonomic approach to denial of service defence. In Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 537-541.
33. Gelenbe, E. and Loukas, G. (2007) Self-aware approach to denial of service defence. Comput. Netw., 51, 1299-1314.