Universiti Teknologi Malaysia Institutional Repository

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

Anazida, Zainal and Den Fairol, Samaon and Mohd Aizaini, Maarof and Siti Mariyam, Shamsuddin (2009) Fuzzy c-means sub clustering with re-sampling in network intrusion detection. In: 2009 Fifth International Conference on Information Assurance and Security, 18 August - 20 September 2009, Xian; China.

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


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:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Anomaly intrusion detection, Classification rates
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:15220
Deposited By: Mrs Liza Porijo
Deposited On:22 Sep 2011 09:51
Last Modified:21 Jun 2017 02:48

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