A feature space analysis for anomaly detection

Author

Jin, Shuyuan ; Yeung, Daniel So ; Wang, Xizhao ; Tsang, Eric C C

Author_Institution

Dept. of Comput., HongKong Polytech. Univ., China

Volume

4

fYear

2005

fDate

10-12 Oct. 2005

Firstpage

3599

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.

Keywords

covariance matrices; security of data; statistical analysis; NN-based anomaly detection; computer systems reliability; covariance matrix feature space; feature space analysis; feature space detector partition; intrusion detection; online detection algorithm; statistical anomaly detection; Computer network reliability; Computer science; Computer vision; Covariance matrix; Detection algorithms; Detectors; Intrusion detection; Machine learning; Mathematics; Space technology; Covariance matrix; anomaly detection; feature space; feature space partition;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2005 IEEE International Conference on

Print_ISBN

0-7803-9298-1

Type

conf

DOI

10.1109/ICSMC.2005.1571706

Filename

1571706