To identify suspicious activity in anomaly detection based on soft computing

Authors:
Witcha Chimphlee;Mohd Noor Md Sap;Abdul Hanan Abdullah;Siriporn Chimphlee;Surat Srinoy
Affiliations:
Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, Thailand;Faculty of Computer Science and Information Systems, University Technology of Malaysia, Skudai, Johor, Malaysia;Faculty of Computer Science and Information Systems, University Technology of Malaysia, Skudai, Johor, Malaysia;Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, Thailand;Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, Thailand
Venue:
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Year:
2006

Citing 10
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Review: The use of computational intelligence in intrusion detection systems: A review

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AsiaCSN '07 Proceedings of the Fourth IASTED Asian Conference on Communication Systems and Networks

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Abstract

The Traditional intrusion detection systems (IDS) look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. However, normal operation often produces traffic that matches likely "attack signature", resulting in false alarms. In this paper we propose an intrusion detection method that proposes rough set based feature selection heuristics and using fuzzy c-means for clustering data. Rough set has to decrease the amount of data and get rid of redundancy. Fuzzy Clustering methods allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to increase accuracy detection rate for suspicious activity and signature detection. Empirical studies using the network security data set from the DARPA 1998 offline intrusion detection project (KDD 1999 Cup) show the feasibility of misuse and anomaly detection results.