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Hierarchical Kohonenen net for anomaly detection in network security.

Suseela T Sarasamma1, Qiuming A Zhu, Julie Huff

  • 1Northrop Grumman Mission Systems, Bellevue, NE 68005, USA. suseela_ts@yahoo.com

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|April 15, 2005
PubMed
Summary

A novel hierarchical Kohonen Map (K-Map) offers efficient intrusion detection by avoiding complex computations. This method improves anomaly detection rates and reduces false positives compared to single-layer K-Maps.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Security

Background:

  • Intrusion detection systems (IDS) are crucial for network security.
  • Traditional anomaly detection methods often involve computationally expensive distance calculations.
  • Existing methods may struggle with large, high-dimensional datasets.

Purpose of the Study:

  • To introduce a novel multilevel hierarchical Kohonen Net (K-Map) for enhanced intrusion detection.
  • To demonstrate the computational efficiency and reduced network size of the proposed hierarchical K-Map.
  • To evaluate the effectiveness of the hierarchical K-Map in detecting network attacks.

Main Methods:

  • The proposed system utilizes a multilevel hierarchical Kohonen Map (K-Map) architecture.

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  • Each level of the hierarchy is a winner-take-all K-Map operating on selected data dimensions.
  • The system was trained and tested using the KDD Cup 1999 benchmark dataset, employing a confidence measure for cluster labeling.

    • Main Results:

    • The hierarchical K-Map demonstrates superior computational efficiency compared to traditional methods like nearest neighbor or K-means.
    • The approach results in a reduced network size, enhancing practicality.
    • The multilevel hierarchical K-Map achieved higher detection rates and lower false positive rates for various attacks than a single-layer K-Map.

    Conclusions:

    • A multilevel hierarchical K-Map provides an efficient and effective solution for intrusion detection.
    • Operating on subsets of the feature space in each layer enhances anomaly detection performance.
    • This method offers a promising alternative to computationally intensive anomaly detection techniques.