Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24364
Title: A second-order statistical detection approach with application to Internet anomaly detection
Authors: Jin, SY
Yeung, DS
Wang, XZ
Keywords: Internet
Computer network management
Covariance matrices
Security of data
Telecommunication security
Telecommunication traffic
Issue Date: 2005
Publisher: IEEE
Source: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, 18-21 August 2005, Guangzhou, China, v. 5, p. 3260-3264 How to cite?
Abstract: Detecting multiple network attacks is essential to intrusion detection, network prevention, security defense and network traffic management. But in today's distributed computer networks, the various and frequent attacks make an effective detection difficult. This paper presents a covariance matrix based second-order statistical method to detect multiple known and unknown network anomalies. The detection method is initially based on the observations of the correlativity changes in typical flooding DoS attacks. It utilizes the difference of covariance matrices among observed samples in the detection. As case studies, extensive experiments are conducted to detect multiple DoS attacks - the prevalent Internet anomalies. The experimental results indicate that the proposed approach achieves high detection rates in detecting multiple known and unknown anomalies.
URI: http://hdl.handle.net/10397/24364
ISBN: 0-7803-9091-1
DOI: 10.1109/ICMLC.2005.1527505
Appears in Collections:Conference Paper

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