Anomaly detection in high-dimensional network data streams: a case study

Zhang, Ji and Gao, Qigang and Wang, Hai (2008) Anomaly detection in high-dimensional network data streams: a case study. In: 2008 IEEE International Conference on Intelligence and Security Informatics (ISI '08), 17-20 June 2008, Taipei, Taiwan.

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[Abstract]: In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Ouliter deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well. Experimental results demonstrate that SPOT is effective in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version deposited in accordance with the copyright policy of the publisher. © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purpose or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 01 Sep 2009 23:30
Last Modified: 02 Jul 2013 23:23
Uncontrolled Keywords: anomaly detection; high dimensional network streams; Stream Projected Outlier deTector; SPOT
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining

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