IMMUNOLOGY-BASED SUBSPACE DETECTORS FOR ANOMALY DETECTION

Xiaoshu Hang and Honghua Dai
Additional contact information
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

Chapter 30 in Challenges in Information Technology Management, 2008, pp 204-212 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractA 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.

Keywords: Information Technology; Knowledge Management; Computing (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.worldscientific.com/doi/pdf/10.1142/9789812819079_0030 (application/pdf)
https://www.worldscientific.com/doi/abs/10.1142/9789812819079_0030 (text/html)
Ebook Access is available upon purchase.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9789812819079_0030

Ordering information: This item can be ordered from

Access Statistics for this chapter

More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().