DDOS detection based on feature space modeling

Proceedings of 2004 International Conference on Machine Learning and Cybernetics

Volumn 7, Issue , 2004, Pages 4210-4215

  Jin, Shu Yuan ^a   Yeung, Daniel S ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Classification; Ddos detection; Feature space; Novel attack, covariance

Indexed keywords

ALGORITHMS; DATA ACQUISITION; FUNCTIONS; MATHEMATICAL MODELS; MATRIX ALGEBRA; SERVERS;

CLASSIFICATION; COVARIANCE; DDOS DETECTION; FEATURE SPACE; NOVEL ATTACK;

SECURITY OF DATA;

EID: 6344229700     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Symantec Internet Security Threat Report. MALIOUS CODE TRENDS-Trends for January 1, 2003-June 30,2003. Symantec INC. http://www.symantec.com.
2. Lincoln Laboratories. 1999 DARPA Intrusion Detection Evaluation. http://www.ll.mit.edu/IST/ideval/index.html.
3. K.Ilgun,R.Kemmerer, P.Porras. State Transition Analysis: A Rule-based Intrusion Detection Approach. IEEE Transactions of Software Engineering, March 1994.
4. H. Javitz, A. Valdes. The NIDES Statistical Component Description and Justification. Technical Report, Computer Scientce Laboraroty, SRI International, Menlo Park, CA, March 1994.
5. J.Xu, W. Lee. Sustaining Availability of Web Services under Distributed Denial of Service Attacks. IEEE Transactions on Computers. Vol. 52, No. 2, Febuary 2003.
6. H. Debar, M. Becker and D. Siboni. A Neural Network Component for An Intrusion Detection System. Proceedings of the 1992 IEEE Computer Society Symposium on Research in Computer Security and Privacy, pp. 240-250, 1992.
7. C. Manikopoulos and S. Papavassiliou. Network Intrusion and Fault Detection: A Statistical Anomaly Approach. IEEE Communications Magazine, October 2002.
8. L. Feinstein and D. Schnackenberg. Statistical Approaches to DDoS Attack Detection and Response. Proceedings of the DARPA Information Survivability Conference and Expostion(DISCEX'03), April 2003.
9. J. Toelle and O. Niggenmann. Supporting Intrusion Detection by Grahp Clustering and Graph Drawing. In the Proceedings of Third International Workshop on Recent Advances in Intrusion Dection RAID 2000, Toulouse, France, October 2000.
10. H. N. Wang, D. Zhang, and K. G. Shin. Detecting SYN Flood Attacks. Proceedings of IEEE INFOCOM'2002. New Yourk City, NY, May 2002.
11. P. Uppuluri, R. Sekar. Experiences with Specifaiction-based Intrusion Detection. Recent Advances in Intrusion Detection (RAID), October 2001.
12. C. Tseng, P. Balasubramanyam, C. Ko. A Specification-based Intrusion Detection System for AODV. The first ACM Workshop Security of Ad Hoc and Sensor Networks Fairfax, Virginia. 2003.
13. S. Cheung, U. Lindqvist, and M. W. Fong. Modeling multistep cyber attacks for scenario recognition. In proceedings of the Third DARPA Information Survivability Conference and Exposition (DISCEX III), Washington, DC., April 2003.
14. B. Morin, H. Debar. Correlation of Intrusion Symptoms: An Application of Chronicles. Recent Advance in Intrusion Detection. Springer-Verlag Berlin Heigelberg 2003.
15. Matthew V. Mahoney and Philip K. Chan. An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection. RAID 2003, LNCS 2820, pp. 220-237, 2003.
16. S. Jin, D. Yeung. A Covariance Analysis Model for DDoS Attack Detection. Proceedings of IEEE ICC'2004. Paris, France, June 2004.