A rough set-based effective rule generation method for classification with an application in intrusion detection

Authors:
Prasanta Gogoi;Dhruba K. Bhattacharyya;Jugal K. Kalita
Affiliations:
Department of Computer Science and Engineering, Tezpur University, Napaam, Tezpur 784028, Assam, India;Department of Computer Science and Engineering, Tezpur University, Napaam, Tezpur 784028, Assam, India;Department of Computer Science, College of Engineering and Applied Science, University of Colorado, Colorado Springs, CO 80918, USA
Venue:
International Journal of Security and Networks
Year:
2013

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Abstract

In this paper, we use Rough Set Theory RST to address the important problem of generating decision rules for data mining. In particular, we propose a rough set-based approach to mine rules from inconsistent data. It computes the lower and upper approximations for each concept, and then builds concise classification rules for each concept satisfying required classification accuracy. Estimating lower and upper approximations substantially reduces the computational complexity of the algorithm. We use UCI ML Repository data sets to test and validate the approach. We also use our approach on network intrusion data sets captured using our local network from network flows. The results show that our approach produces effective and minimal rules and provides satisfactory accuracy.