Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30011
Title: A feature space analysis for anomaly detection
Authors: Jin, SY
Yeung, DS
Wang, XZ
Tsang, ECC
Keywords: Covariance matrices
Security of data
Statistical analysis
Issue Date: 2005
Publisher: IEEE
Source: 2005 IEEE International Conference on Systems, Man and Cybernetics, 10-12 October 2005, v. 4, p. 3599-3603 How to cite?
Abstract: Intrusion detection is an important part of assuring the reliability of computer systems. From the viewpoint of feature space partition of detectors, this paper investigates one of the limitations of two traditional anomaly detection technologies - NN-based anomaly detection and statistical detection approaches in detecting novel attacks. A high dimensional covariance matrix feature space and an on-line detection algorithm are proposed to detect various known and unknown attacks. An illustrative example of detecting various known and unknown probing attacks is provided.
URI: http://hdl.handle.net/10397/30011
ISBN: 0-7803-9298-1
DOI: 10.1109/ICSMC.2005.1571706
Appears in Collections:Conference Paper

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