Anomaly detection in high-dimensional network data streams: a case study
Paper
Paper/Presentation Title | Anomaly detection in high-dimensional network data streams: a case study |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Gao, Qigang (Author) and Wang, Hai (Author) |
Journal or Proceedings Title | Proceedings of the 2008 IEEE International Conference on Intelligence and Security Informatics (ISI '08) |
Number of Pages | 3 |
Year | 2008 |
Place of Publication | New York, United States |
ISBN | 1424424153 |
Web Address (URL) of Paper | http://isi2008.cpu.edu.tw/ |
Conference/Event | 2008 IEEE International Conference on Intelligence and Security Informatics (ISI '08) |
Event Details | 2008 IEEE International Conference on Intelligence and Security Informatics (ISI '08) Event Date 17 to end of 20 Jun 2008 Event Location Taipei, Taiwan |
Abstract | [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. |
Keywords | anomaly detection; high dimensional network streams; Stream Projected Outlier deTector; SPOT |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Dalhousie University, Canada |
Saint Mary's University, Canada |
https://research.usq.edu.au/item/9z27q/anomaly-detection-in-high-dimensional-network-data-streams-a-case-study
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