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Title: DDoS detection based on feature space modeling
Authors: Jin, S
Yeung, DS
Issue Date: 2004
Source: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 26-29 August 2004, v. 7, p. 4210-4215
Abstract: This work tries to use a feature space modeling methodology to identify DDoS attacks. Compared with the existing approaches, the proposed feature space presents a more general model in DDoS detection. It changes the non-separable attacks into separable cases and more importantly, it also allows the unknown attacks potentially being identified by their own features. To validate these claims, a classification algorithm is defined under this feature space. We use a subset in KDD Cup 1999 data in the experiments. The KDD Cup 1999 training dataset contains 6 different types of DDoS attacks and the testing dataset contains more 4 novel DDoS attacks. In detecting these 6 already known DDoS attacks and 4 novel DDoS attacks from the normal, we get a high detection rate under this feature space by using the proposed classification algorithm, which shows the discriminative abilities of the feature space.
Keywords: Computer networks
Covariance matrices
Security of data
Telecommunication security
Publisher: IEEE
ISBN: 0-7803-8403-2
DOI: 10.1109/ICMLC.2004.1384578
Appears in Collections:Conference Paper

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