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Immunology-Based Subspace Detectors For Anomaly Detection

In: Challenges In Information Technology Management

Author

Listed:
  • XIAOSHU HANG

    (School of Engineering and Information Technology Deakin University, 221 Burwood Highway, Burwood Melbourne, Vic 3125, Australia)

  • HONGHUA DAI

    (School of Engineering and Information Technology Deakin University, 221 Burwood Highway, Burwood Melbourne, Vic 3125, Australia)

Abstract

A key problem in high dimensional anomaly detection is that the time spent in constructing detectors by the means of generate-and-test is intolerable. In fact, due to the high sparsity of the data, it is ineffective to construct detectors in the whole data space. Previous investigations have shown that most essential patterns can be discovered in different subspaces. This inspires us to construct detectors in significant subspaces only for anomaly detection. We first use ENCLUS-based method to discover all significant subspaces and then use a greedy-growth algorithm to construct detectors in each subspace. The elements used to constitute a detector are grids instead of data points, which makes the time-consumption irrelevant to the size of the normal data. We test the effectiveness and efficiency of our method on both synthetic and benchmark datasets. The results reveal that our method is particularly useful in anomaly detection in high dimensional data spaces.

Suggested Citation

  • Xiaoshu Hang & Honghua Dai, 2008. "Immunology-Based Subspace Detectors For Anomaly Detection," World Scientific Book Chapters, in: Man-Chung Chan & Ronnie Cheung & James N K Liu (ed.), Challenges In Information Technology Management, chapter 30, pages 204-212, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812819079_0030
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