Reduction of data size in intrusion domain using modified simulated annealing fuzzy clustering algorithm

Abstract

Network security is becoming an important issue as the size and application of the network is exponentially increasing worldwide. Performance of Intrusion Detection System (IDS) is greatly depends on the size of data and a systematic approach to handling such data. In the paper, modified simulated annealing fuzzy clustering (SAFC) algorithm has been proposed using the concept of Rough set theory that removes randomness of the SAFC algorithm and applied on intrusion domain for data size reduction. The reduced data set increases classification accuracy in detecting network data set as 'anomaly' or 'normal' compared to the original data set. Davies-Bouldin (DB) validity Index is evaluated to measure the performance of the proposed IDS. © 2013 Springer-Verlag.