A framework for classifying denial of service attacks
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
MULTOPS: a data-structure for bandwidth attack detection
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
A comprehensive taxonomy of DDOS attacks and defense mechanism applying in a smart classification
WSEAS Transactions on Computers
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
Distributed denial of service attack detection using an ensemble of neural classifier
Computer Communications
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Although the prevention of Distributed Denial of Service (DDoS) attacks is not possible, detection of such attacks plays main role in preventing their progress. In the flooding attacks, especially new sophisticated DDoS, the attacker floods the network traffic toward the target computer by sending pseudo-normal packets. Therefore, multi-purpose IDSs do not offer a good performance (and accuracy) in detecting such kinds of attacks. In this paper, a novel method for detection of DDoS attacks has been introduced based on a statistical pre-processor and an unsupervised artificial neural net. In addition, SPUNNID system has been designed based on the proposed method. The statistical pre-processing has been used to extract some statistical features of the traffic, showing the behavior of DDoS attacks. The unsupervised neural net is used to analyze and classify them as either a DDoS attack or normal. Moreover, the method has been more investigated using attacked network traffic, which has been provided from a real environment. The experimental results show that SPUNNID detects DDoS attacks accurately and efficiently.