Research Output

Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis

  The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order statistics from the observed network traffic records. These second-order statistics extracted by the proposed analysis approach can provide important correlative information hiding among the features. By making use of this hidden information, the detection accuracy can be significantly enhanced. The effectiveness of the proposed multivariate correlation analysis approach is evaluated on the KDD CUP 99 dataset. The evaluation shows encouraging results with average 99.96% detection rate and 2.08% false positive rate. Comparisons also show that our multivariate correlation analysis based detection approach outperforms some other current researches in detecting DoS attacks.

  • Date:

    31 December 2011

  • Publication Status:

    Published

  • Publisher

    Springer Berlin Heidelberg

  • DOI:

    10.1007/978-3-642-24965-5_85

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    005.8 Data security

  • Funders:

    Edinburgh Napier Funded

Citation

Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2010). Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis

Authors

Keywords

Denial-of-Service Attack, Euclidean Distance Map, Multivariate Correlations, Anomaly Detection

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