Research Output
A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis
  Interconnected systems, such as Web servers, database servers, cloud computing servers and so on, are now under threads from network attackers. As one of most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on these computing systems. In this paper, we present a DoS attack detection system that uses multivariate correlation analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-area-based technique is proposed to enhance and to speed up the process of MCA. The effectiveness of our proposed detection system is evaluated using KDD Cup 99 data set, and the influences of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The results show that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy.

  • Type:


  • Date:

    28 February 2014

  • Publication Status:


  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:


  • ISSN:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    005.8 Data security


Tan, Z., Jamdagni, A., He, X., Nanda, P., & Ping Liu, R. (2014). A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis. IEEE Transactions on Parallel and Distributed Systems, 25(2), 447-456.



multivariate correlations, triangle area, Denial-of-service attack, network traffic characterization

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