Abstract
DDoS (Distributed Denial-of-Service) attacks detection system deployed in source-end network is superior in detection and prevention than that in victim network, because it can perceive and throttle attacks before data flow to Internet. However, the current existed works in source-end network lead to a high false-positive rate and false-negative rate for the reason that they are based on single-feature, and they couldn’t synthesize multi-features simultaneously. This paper proposes a novel approach using Multi-stream Fused Hidden Markov Model (MF-HMM) on source-end DDoS detection for integrating multi-features simultaneously. The multi-features include the S-D-P feature, TCP header Flags, and IP header ID field. Through experiments, we compared our original approach based on multiple detection feature with other main algorithms (such as CUSUM and HMM) based on single-feature. The results present that our approach effectively reduces false-positive rate and false-negative rate, and improve the precision of detection.
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References
Jelena, M.: D-WARD. Source-End Defense Against Distributed Denial-of-Service Attacks, CSD of UCLA, pp. 101–125 (2003)
Kang, J., Zhang, Z., Ju, J.-b.: Protect e-commerce against DDoS attacks with improved D-WARD detection system. In: IEEE International Conference on e-Technology, e-Commerce and e-Service, Hong Kong (April 2005)
Peng, T., Leckie, C., Ramamohanarao, K.: Detecting Distributed Denial of Service Attacks Using Source IP Address Monitoring. In: Mitrou, N.M., Kontovasilis, K., Rouskas, G.N., Iliadis, I., Merakos, L. (eds.) NETWORKING 2004. LNCS, vol. 3042, pp. 771–782. Springer, Heidelberg (2004)
Zhou, D., Zhang, H.: A DDoS Attack Detection Method Based on Hidden Markov Model. Journal of Computer Research and Development 42, 1594–1599 (2005)
Moore, D., Voelker, G., Savage, S.: Inferring internet denial-of-service activity. In: The 10th USENIX Security Symposium, Washington (2001)
Chang-Han, J., Shiuh-Pyng, S.: Detecting Distributed DoS/Scanning by Anomaly Distribution of Packet Fields. In: International Computer Symposium 2002 (2002)
Zeng, Z., Tu, J.: Pianfetti: Audio-visual affect recognition through multi-stream fused HMM for HCI. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (June 2005)
Pan, H., Levinson, S., Huang, T.S., Liang, Z.P.: A fused Hidden Markov Model With Application to Bimodal Speech Processing. IEEE Transaction on Signal Processing 52(3), 573–581 (2004)
Brand, M., Oliver, N.: Coupled hidden Markov models for complex action recognition. Computer Vision Pattern Recognition, 201–206 (1997)
Saul, L.K., Jordan, M.I.: Mixed memory Markov model: Decomposing complex stochastic processes as mixture of simpler ones. Machine Learning 37, 75–88 (1999)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of IEEE 77(2) (February 1989)
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Kang, J., Zhang, Y., Ju, Jb. (2006). Detecting DDoS Attacks Based on Multi-stream Fused HMM in Source-End Network. In: Pointcheval, D., Mu, Y., Chen, K. (eds) Cryptology and Network Security. CANS 2006. Lecture Notes in Computer Science, vol 4301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11935070_24
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DOI: https://doi.org/10.1007/11935070_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49462-1
Online ISBN: 978-3-540-49463-8
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