Discriminating DDoS attacks from flash crowds using flow correlation coefficient

Shui Yu, Wanlei Zhou, Weijia Jia, Song Guo, Yong Xiang, Feilong Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

177 Citations (Scopus)


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.
Original languageEnglish
Article number6060809
Pages (from-to)1073-1080
Number of pages8
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number6
Publication statusPublished - 27 Mar 2012
Externally publishedYes


  • DDoS attacks
  • discrimination
  • flash crowds
  • similarity

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


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