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Discriminating DDoS attacks from flash crowds using flow correlation coefficient

journal contribution
posted on 2012-04-25, 00:00 authored by Shui Yu, Wanlei Zhou, W Jia, S Guo, Yong XiangYong Xiang, F Tang
Distributed Denial of Service (DDoS) attack is a critical threat to the Internet, and botnets are usually the engines behind them. Sophisticated botmasters attempt to disable detectors by mimicking the traffic patterns of flash crowds. This poses a critical challenge to those who defend against DDoS attacks. In our deep study of the size and organization of current botnets, we found that the current attack flows are usually more similar to each other compared to the flows of flash crowds. Based on this, we proposed a discrimination algorithm using the flow correlation coefficient as a similarity metric among suspicious flows. We formulated the problem, and presented theoretical proofs for the feasibility of the proposed discrimination method in theory. Our extensive experiments confirmed the theoretical analysis and demonstrated the effectiveness of the proposed method in practice.

History

Journal

IEEE transactions on parallel and distributed systems

Volume

23

Issue

6

Pagination

1073 - 1080

Publisher

IEEE

Location

Piscataway, N. J.

ISSN

1045-9219

eISSN

1558-2183

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2012, IEEE