Intrusion Detection Models Based on Data Mining

DOI

10.1080/18756891.2012.670519How to use a DOI?

Keywords

Intrusion detection, data mining, frequency pattern, tree pattern

Abstract

Computer intrusions are taking place everywhere, and have become a major concern for information security. Most intrusions to a computer system may result from illegitimate or irregular calls to the operating system, so analyzing the system-call sequences becomes an important and fundamental technique to detect potential intrusions. This paper proposes two models based on data mining technology, respectively called frequency patterns () and tree patterns () for intrusion detection. employs a typical method of sequential mining based on frequency analysis, and uses a short sequence model to find out quickly frequent sequential patterns in the training system-call sequences. makes use of the technique of tree pattern mining, and can get a quality profile from the training system-call sequences of a given system. Experimental results show that has good performances in training and detecting intrusions from short system-call sequences, and can achieve a high detection precision in handling long sequences.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Guojun Mao
AU  - Xindong Wu
AU  - Xuxian Jiang
PY  - 2012
DA  - 2012/02/01
TI  - Intrusion Detection Models Based on Data Mining
JO  - International Journal of Computational Intelligence Systems
SP  - 30
EP  - 38
VL  - 5
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2012.670519
DO  - 10.1080/18756891.2012.670519
ID  - Mao2012
ER  -