Please use this identifier to cite or link to this item: http://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.
RMID: 0030085597
DOI: 10.2298/CSIS170201018H
Grant ID: http://purl.org/au-research/grants/arc/DP150104871
Appears in Collections:Computer Science publications

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