Research Output
An Intrusion Detection System Based on Polynomial Feature Correlation Analysis
  This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic related statistics in order to provide distinguishing features for detection. The proposed IDS was evaluated using the well-known KDD Cup 99 data set. Evaluation results show that the proposed system achieved better detection rates on KDD Cup 99 data set in comparison with another two state-of-the-art detection schemes. Moreover, the computational complexity of the system has been analysed in this paper and shows similar to the two state-of-the-art schemes.

  • Date:

    11 September 2017

  • Publication Status:


  • Publisher

    Institute of Electrical and Electronics Engineers

  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    005.8 Data security

  • Funders:

    Edinburgh Napier Funded


Li, Q., Tan, Z., Jamdagni, A., Nanda, P., He, X., & Han, W. (2017). An Intrusion Detection System Based on Polynomial Feature Correlation Analysis. In 2017 IEEE Trustcom/BigDataSE/I​SPA Conference Proceedings



Intrusion Detection System (IDS), polynomial, feature correlation analysis, Mahalanobis distance, computational complexity

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