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Fuzzy c-means sub-clustering with re-sampling in network intrusion detection

Zainal, Anazida and Samaon, Den Fairol and Maarof, Mohd. Aizaini and Shamsuddin, Siti Mariyam (2009) Fuzzy c-means sub-clustering with re-sampling in network intrusion detection. In: 2009 Fifth International Conference on Information Assurance and Security. Article number 5283185, 1 . IEEE, pp. 683-686. ISBN 978-076953744-3

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Official URL: http://dx.doi.org/10.1109/IAS.2009.333

Abstract

Both supervised and unsupervised learning are popularly used to address the classification problem in anomaly intrusion detection. The classical and challenging task in intrusion detection is how to identify and classify new attacks or variants of normal traffic. Though the classification rate is not at par with supervised approach, unsupervised approach is not affected by the unknown attacks. Inspired by the success of bagging technique used in prediction, the study deployed similar re-sampling strategy by splitting the training data into half. Data was obtained from KDDCup 1999 dataset. The finding shows that re-sampling improves performance of Fuzzy c-Means sub-clustering.

Item Type:Book Section
Additional Information:5th International Conference on Information Assurance and Security, IAS 2009; Xian; 18 August 2009 through 20 September 2009
Uncontrolled Keywords:anomaly intrusion detection, classification rates, data sets, fuzzy c mean, in-network
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:14624
Deposited By: Ms Zalinda Shuratman
Deposited On:30 Sep 2011 15:20
Last Modified:30 Sep 2011 15:20

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