Abstract
The Intrusion Detection System (IDS) deals with the huge amount of network data that includes redundant and irrelevant features causing slow training and testing procedure, higher resource usage and poor detection ratio. Feature selection is a vital preprocessing step in intrusion detection. Hence, feature selec-tion is an essential issue in intrusion detection and need to be addressed by selec-ting the appropriate feature selection algorithm. A major challenge to select the optimal feature selection methods can precisely calculate the relevance of fea-tures to the detection process and the redundancy among features. In this paper, we study the concepts and algorithms used for feature selection algorithms in the IDS. We conclude this paper by identifying the best feature selection algorithm to select the important and useful features from the network dataset.
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References
Barry, B.I.A. ad Chan, H.A., Intrusion Detection Systems in Handbook of Information and Communication Security, Stavroulakis, P. and Stamp, M., Eds., Springer Berlin Heidelberg, 2010, pp. 193–205.
Tavallaee, M., Bagheri, E., Wei, L., and Ghorbani, A.A., A detailed analysis of the KDD CUP 99 data set, CISDA 2009. IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, pp. 1–6.
KDD Cup. http://kddicsuciedu/databases/kddcup99/kddcup99html. Cited April 2, 2015.
Stolfo, S.J., Fan, W., Lee, W., Prodromidis, A., and Chan, P.K., Cost-based modeling for fraud and intrusion detection: Results from the JAM project, DISCEX'00. Proceedings of DARPA Information Survivability Conference and Exposition, 2000, pp. 130–144.
Lippmann, R.P., Fried, D.J., Graf, I., Haines, J.W., Kendall, K.R., Mcclung, D., et al., Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation DISCEX'00. Proceedings DARPA Information Survivability Conference and Exposition, 2000, pp. 12–26.
Labs, M.L., 2008. DARPA Intrusion Detection Evaluation, 1998http://wwwllmitedu/mission/communications/ist/corpora/ideval/indexhtml. Cited April 2, 2015.
Kandeeban, S.S. and Rajesh, R.S., Integrated intrusion detection system using soft computing Int. J. Network Secur., vol. 10, pp. 87–92, 2010.
Hoque, M.S., Mukit, M., Bikas, M. and Naser, A., An implementation of intrusion detection system using genetic algorithm, arXiv preprint. arXiv:1204.1336, 2012.
Guo, Y., Wang, B., Zhao, X., Xie, X., Lin, L., and Zhou, Q., Feature selection based on Rough set and modified genetic algorithm for intrusion detection 5th International Conference on Computer Science and Education (ICCSE), 2010, pp. 1441–1446.
Yi, Z. and Li-Jun, Z., A rule generation model using S-PSO for misuse intrusion detection, International Conference on Computer Application and System Modeling (ICCASM), 2010, vol. 3, pp. 418–423.
Ning, L. and Jianhua, Z., Intrusion detection research based on improved PSO and SVM International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), 2012, pp. 1263–1266.
Li, W. and Meng, Y., Improving the performance of neural networks with random forest in detecting network intrusions in Advances in Neural Networks–ISNN 2013, Guo, C., Hou, Z.-G., and Zeng, Z., Eds., Springer Berlin Heidelberg, 2013, vol. 7952, pp. 622–629.
Hasan, M.A.M., Nasser, M., Pal, B., and Ahmad, S., Support vector machine and random forest modeling for intrusion detection system (IDS) J. Intell. Learning Syst. Appl., 2014, vol. 2014.
Eesa, A.S., Brifcani, A.M.A., and Orman, Z., Cuttlefish Algorithm-A novel bio-inspired optimization algorithm Int. J. Sci. Eng. Res., 2013, vol. 4, pp. 1978–1986.
Eesa, A.S., Orman, Z., and Brifcani, A.M.A., A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems, Expert Syst. Appl., 2015, vol. 42, pp. 2670–2679.
Kumar, K., Kumar, G., and Kumar, Y., Feature selection approach for intrusion detection system Int. J. Adv. Trends Comput. Sci. Eng., 2013, vol. 2, pp. 47–53.
Tiwari, R. and Singh, M.P., Correlation-based attribute selection using genetic algorithm, Int. J. Comput. Appl., 2010, vol. 4, pp. 8875–8887.
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Anusha, K., Sathiyamoorthy, E. Comparative study for feature selection algorithms in intrusion detection system. Aut. Control Comp. Sci. 50, 1–9 (2016). https://doi.org/10.3103/S0146411616010028
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DOI: https://doi.org/10.3103/S0146411616010028