Research on Anomaly Intrusion Detection Based on Rough Set Attribute Reduction

DOI

10.2991/iccasm.2012.154How to use a DOI?

Keywords

Rough set, Attribute reduction, Anomaly Intrusion Detection, Classification

Abstract

With growing amount of network information flowing, the limitations of the traditional network IDSs are more and more outstanding, it couldn’t adapt to the trend of increasing novel network attacks and data quantity. Rough Set’s reduction theory can effectively avoid redundancy, reduce the extra attributes in the large information. In order to find the novel attacks in IDSs, this paper presents the method of how to measure distance with rough set theory, and the results show that it is efficient to find anomaly.

Copyright

© 2012, 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  - CONF
AU  - Cuijuan Liu
AU  - Yuanyuan Li
AU  - Yankai Qin
PY  - 2012/08
DA  - 2012/08
TI  - Research on Anomaly Intrusion Detection Based on Rough Set Attribute Reduction
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 607
EP  - 610
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.154
DO  - 10.2991/iccasm.2012.154
ID  - Liu2012/08
ER  -