The Application of Rough Sets on Network Intrusion Detection

With a growing amount of network information flowing, the limitations of the traditional network IDSs become more and more obvious, which can not adapt to the increasing trend of novel network attacks and data quantities. As a result, the analysis process becomes time-consuming. Fortunately, as is w...

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Bibliographic Details
Published in2007 International Conference on Machine Learning and Cybernetics Vol. 7; pp. 3657 - 3660
Main Author Cui-Juan Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Attributes reduction
Cybernetics
Data engineering
Educational institutions
Environmental economics
Information entropy
Intrusion detection
Machine learning
Network intrusion detection
Pattern analysis
Rough sets
Set theory
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Summary:With a growing amount of network information flowing, the limitations of the traditional network IDSs become more and more obvious, which can not adapt to the increasing trend of novel network attacks and data quantities. As a result, the analysis process becomes time-consuming. Fortunately, as is well known, rough set's reduction theory can effectively avoid redundancy and reduce extra attributes. Therefore, to solve the problems in IDSs, the paper advocates using the theory of rough set to improve the attribute reduction algorithm. Experimental results show that the number of attributes can be reduced 64% using the proposed method. Thus, it can be concluded that the presented method can shorten detection process efficiently.
ISBN:1424409721
9781424409723
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370782