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Integrating genetic algorithms and fuzzy c-means for anomaly detection

Chimphlee, Witcha and Abdullah, Abdul Hanan and Sap, Noor Md. and Chimphlee, Siriporn and Srinoy, Surat (2005) Integrating genetic algorithms and fuzzy c-means for anomaly detection. In: Proceedings of INDICON 2005: An International Conference of IEEE India Council .

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Official URL: http://dx.doi.org/10.1109/INDCON.2005.1590237


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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Anomaly detection, fuzzy c-means, genetic algorithms, unsupervised clustering
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System
ID Code:7451
Deposited By: Maznira Sylvia Azra Mansor
Deposited On:05 Jan 2009 06:43
Last Modified:28 Aug 2017 08:36

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