U. Ravale, N. Marathe, P. Padiya, "Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function. ", In proceedings of the International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 428-435, 2015.
 A. A. Aburomman, M. B. I. Reaz, "A Novel Weighted Support Vector Machines Multiclass Classifier Based on Differential Evolution for Intrusion Detection Systems", Information Sciences, vol. 414, pp. 225-246, 2017.
 A. Sharma, I. Manzoor, N. Kumar, "A Feature Reduced Intrusion Detection System Using ANN Classifier", Expert Systems with Applications, vol. 88, pp. 249-257, 2017.
 Y. Zhu, J. Liang, J. Chen, Z. Ming, "An improved NSGA-III algorithm for feature selection used in intrusion detection", Knowledge-Based Systems, vol. 116, pp. 74-85, 2017.
 S. N. Ghazavi, T. W. Liao, "Medical data mining by fuzzy modeling with selected features", Artificial Intelligence in Medicine, vol. 43, pp. 195-206, 2008.
 T.N. Lal, O. Chapelle, J. Weston, A. Elisseeff, Embedded methods, in: I. Guyon, S. Gunn, M. Nikravesh, L.A. Zadeh (Eds.), Feature Extraction: Foundations and Applications. Studies in Fuzziness and Soft Computing, vol. 207, Springer, Berlin, Heidelberg, pp. 137–165, 2006.
 Ch. Khammassi, S. Krichen. "A GA-LR Wrapper Approach for Feature Selection in Network Intrusion Detection.", Computers & Security, 2017.
 Y. Y. Chung, N. Wahid, "A hybrid network intrusion detection system using simplified swarm optimization (sso)", Applied Soft Computing, vol. 12, pp. 3014–3022, 2012.
 E. De la Hoz, A. Ortiz, J. Ortega, A. Martínez-Álvarez, "Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps.", Knowledge-Based Systems, vol. 71, pp. 322–338, 2014.
 S. H. Kang, K. J. Kim, "A feature selection approach to find optimal feature subsets for the network intrusion detection system", Cluster Computing, pp. 1–9, 2016.
P. Maji, P. Garai, “On fuzzy-rough attribute selection: Criteria of Max-Dependency, Max-Relevance, MinRedundancy, and Max-Significance.”, applied soft computing, vol. 13, pp. 3968-3980, 2013.  Z. Pawlak, A. Skowron, “Rudiments of rough sets”, Information sciences, vol.177, pp. 3-27, 2007.
 G.A. Montazer, S. ArabYarmohammadi, "Detection of phishing attacks in Iranian e-banking using a fuzzy–rough hybrid system", Applied Soft Computing, vol. 35, pp. 482–492, 2015.
 C.H. Xie, Y.J. Liu, J.Y. Chang, "Medical image segmentation using rough set and local polynomial regression", Multimedia Tools and Applications, vol. 74, pp. 1885–1914, 2015.
 V. Prasad, T.S. Rao, M.S. Babu, "Thyroid disease diagnosis via hybrid architecture composing rough data sets theory and machine learning algorithms", Soft Computing, vol. 20, pp. 1179–1189, 2016.
 M.P. Francisco, J.V. Berna-Martinez, A.F. Oliva, M.A.A. Ortega, "Algorithm for the detection of outliers based on the theory of rough sets", Decision Support Systems, vol. 75, pp. 63–75, 2015.
 J. Dai, Q. Xu, "Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification", Applied Soft Computing, vol. 13, pp. 1184-1199, 2012.
 F. Amiri, M. Rezaei, C. Lucus. A. Shakeri, N. Yazdani, "Mutual information-based feature selection for intrusion detection systems", Network and computer applications, vol. 34, pp. 1184-
 İ. Özçelik
, R. R. Brooks, "Deceiving entropy based DoS detection", computers & security
, vol. 48, pp. 234-245, 2015.
 Sh. Aljawarneh, M. Aldwairi, M. B. Yassein, "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model", Computational Science, 2017.
 M. Dorigo, L. M. Gambardella, "A cooperative learning approach to the traveling salesman problem", IEEE Transactions on Evolutionary Computation, vol. 1, pp. 53-66, 1997.
 A. Shenfield, D. Day, A. Ayesh, "Intelligent intrusion detection systems using artificial neural networks.", the Korean Institute of Communications and Information Sciences, vol. 4, pp. 95-99, 2018.
 M. Hosseinzadeh, P. Kabiri. "Feature selection for intrusion detection system using ant colony optimization", International Journal of Network Security, vol. 18, pp. 420-432, 2016.