Abstract
This paper investigates the performance of different feature selection techniques such as ranking and subset-based techniques, aiming to find the optimum collection of features to detect attacks with an appropriate classifier. The results reveal that more accuracy of detection and less false alarms are obtained after eliminating the redundant features and determining the most useful set of features, which increases the intrusion detection system (IDS) performance.
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References
Sen, S. (2015). A survey of intrusion detection systems using evolutionary computation. In X. S. Yang, S. F. Chien, & T. O. Ting (Eds.), Bio-inspired computation on telecommunication (pp. 73–94). Burlington: Morgan Kaufmann.
Gyanchandani, M., Yadav, R. N., & Rana, J. L. (2010). Intrusion detection using C4. 5: performance enhancement by classifier combination. International Journal on Signal and Image Processing,1(03), 46–49.
Garg, T., & Khurana, S. S. (2014). Comparison of classification techniques for intrusion detection dataset using WEKA. In International conference on recent advances and innovations in engineering (ICRAIE-2014) (pp. 1–5). IEEE.
Garge, T., & Kumar, Y. (2014). Combinational feature selection approach for network intrusion detection system. In International conference on parallel (pp. 82–87).
Tavallaee, M., & Bagheri, E., Lu, W., & Ghorbani, A. (2009) A detailed analysis of the KDD CUP 99 dataset. In The second IEEE symposium on computational intelligence.
http://TheNSL-KDDDataset.htm Accessed April 11, 2016.
Elrawy, M. F., Abdelhamid, T. K., & Mohamed, A. M. (2013). IDS in telecommunication network using PCA. International Journal of Computer Networks & Communications (IJCNC),5(4), 147–157.
Zargar, Gh, & Baghaie, T. (2012). Category-based intrusion detection using PCA. Journal of Information Security,3, 259–271.
www.softcomputing.net. Accessed August 1, 2015.
Liu, H. W., Suna, J. G., Liu, L., & Zhang, H. J. (2009). Feature selection with dynamic mutual information. Pattern Recognition,42, 1330–1339.
Liu, H., Motoda, H., Setiono, R., & Zhao, Z. (2010) Feature selection: An eve evolving frontier in data mining. In Workshop and conference proceedings (Vol. 4, pp. 4–13), Publisher Citeseer.
Hall, M. (1999). Correlation based feature selection for machine learning. In: Doctoral Dissertation, Department of Computer Science, University of Waikato.
Thanah, H., Franke, K. S. (2012). Pertovic feature extraction methods for intrusion detection systems. In Threats countermeasures and advances in applied information security (pp. 23–52).
Sri Harsha, V. (2010). Ensemble of feature selection techniques for high dimensional data Published Master’s Thesis. Western Kentucky University.
Wang, Y., & Makedon, F. (2004). Application of relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data. In Proceedings 2004 IEEE computational systems bioinformatics conference, 2004. CSB 2004. (pp. 497–498). IEEE.
Neethu, B. (2013). Classification of intrusion detection dataset using machine learning approaches, IJECSE.
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Ghazy, R.A., El-Rabaie, ES.M., Dessouky, M.I. et al. Feature Selection Ranking and Subset-Based Techniques with Different Classifiers for Intrusion Detection. Wireless Pers Commun 111, 375–393 (2020). https://doi.org/10.1007/s11277-019-06864-3
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DOI: https://doi.org/10.1007/s11277-019-06864-3