Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/112071
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Type: | Journal article |
Title: | Promising techniques for anomaly detection on network traffic |
Author: | Tian, H. Liu, J. Ding, M. |
Citation: | Computer Science and Information Systems, 2017; 14(3):597-609 |
Publisher: | ComSIS Consortium |
Issue Date: | 2017 |
ISSN: | 1820-0214 2406-1018 |
Statement of Responsibility: | Hui Tian, Jingtian Liu and Meimei Ding |
Abstract: | In various networks, anomaly may happen due to network breakdown, intrusion detection, and end-to-end traffic changes. To detect these anomalies is important in diagnosis, fault report, capacity plan and so on. However, it’s challenging to detect these anomalies with high accuracy rate and time efficiency. Existing works are mainly classified into two streams, anomaly detection on link traffic and on global traffic. In this paper we discuss various anomaly detection methods on both types of traffic and compare their performance. |
Keywords: | Diffusion wavelet; principal component analysis; anomaly detection |
Rights: | Computer Science and Information Systems is an Open Access journal. All articles can be downloaded free of charge and used in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY NC ND) License. |
DOI: | 10.2298/CSIS170201018H |
Grant ID: | http://purl.org/au-research/grants/arc/DP150104871 |
Published version: | http://dx.doi.org/10.2298/csis170201018h |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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hdl_112071.pdf | Published Version | 616.12 kB | Adobe PDF | View/Open |
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