NDLI: Integrating Genetic Algorithms and Fuzzy c-Means for Anomaly Detection

Content Provider	IEEE Xplore Digital Library
Author	Chimphlee, W. Abdullah, A.H. Noor Md Sap, M. Chimphlee, S. Srinoy, S.
Copyright Year	2005
Description	Author affiliation: Faculty of Science and Technology, Suan Dusit Rajabhat University, 295 Rajasrima Road, Dusit, Bangkok, Thailand, Tel: (Chimphlee, W.)
Abstract	The goal of intrusion detection is to discover unauthorized use of computer systems. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate. We used data set from 1999 KDD intrusion detection contest.
Starting Page	575
Ending Page	579
File Size	813343
Page Count	5
File Format	PDF
ISBN	0780395034
DOI	10.1109/INDCON.2005.1590237
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-11
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Data mining Face detection Anomaly detection Genetic algorithms Fuzzy c-means Intrusion detection Clustering algorithms Computer networks Labeling Detection algorithms Genetic Algorithms Fuzzy systems Unsupervised clustering
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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