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.
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References
Bandyopadhyay, S.: Simulated Annealing Using a Reversible Jump Markov Chain Monte Carlo Algorithm for Fuzzy Clustering. IEEE Transactions on Knowledge and Data Engineering 17(4) (April 2005)
Zhu, L., Chung, F.L., Wang, S.: Generalized Fuzzy C-Means Clustering Algorithm With Improved Fuzzy Partitions. IEEE Transactions on Systems, Man and Cybernetics 39(3), 578–591 (2009)
Kirkpatrik, S., Gelatt, C., Vecchi, M.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Analysis and Machine Intelligence 13, 841–847 (1991)
Wang, X.Y., Whitwell, G., Garibaldi, J.M.: The Application Of A Simulated Annealing Fuzzy Clustering Algorithm For Cancer Diagnosis. In: IEEE 4th International Conference on Intelligent Systems Design and Application, Budapest, Hungary, August 26-28, pp. 467–472 (2004)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Recognition and Machine Intelligence 1(2), 224–227 (1979)
Guo, P., Tanaka, H.: Upper and lower possibility distributions with rough set concepts. In: Inuiguchi, M., Hirano, S., Tsumoto, S. (eds.) Rough Set Theory and Granular Computing. LNCS, pp. 243–250. Springer (2002)
The NSL-KDD Data Set, http://iscx.ca/NSL-KDD/
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Sengupta, N., Srivastava, A., Sil, J. (2013). Reduction of Data Size in Intrusion Domain Using Modified Simulated Annealing Fuzzy Clustering Algorithm. In: Das, V.V., Chaba, Y. (eds) Mobile Communication and Power Engineering. AIM 2012. Communications in Computer and Information Science, vol 296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35864-7_14
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DOI: https://doi.org/10.1007/978-3-642-35864-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35863-0
Online ISBN: 978-3-642-35864-7
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