Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Protect E-Commerce against DDoS Attacks with Improved D-WARD Detection System
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Audio-Visual Affect Recognition through Multi-Stream Fused HMM for HCI
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Inferring internet denial-of-service activity
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
A fused hidden Markov model with application to bimodal speech processing
IEEE Transactions on Signal Processing
Genetic algorithm combined with support vector machine for building an intrusion detection system
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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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.