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Research on feature weights of fuzzy c-means algorithm and its application to intrusion detection

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2 Author(s)
Jian Yang ; Dept. of Comput. Sci. & Technol., Dezhou Univ., Dezhou, China ; Yufu Ning

The fuzzy c-means (FCM) clustering algorithm is more suitable for intrusion detection, but the standard FCM does not consider the characteristics of each feature and the contribution rate to clustering analysis when calculating the distance between two samples, this obviously affects the authenticity and accuracy of the classification. Aim at the actual situation of intrusion detection data, a new weight calculation method is introduced in the paper, the method considers that there are independence factors exist for each feature of the sample, also the weight assignment of each feature should be related to the degree of its independence; while the independence degree of each feature depends on the cohesion and coupling of its value space. Simulation experiments shows that the new weight calculation method has higher classification accuracy, in practice it is very effective.

Published in:

Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on  (Volume:3 )

Date of Conference:

17-18 July 2010