S. Ganapathy, K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi P. Yogesh, and A. Kannan, “ Intelligent feature selection and classification techniques for intrusion detection in networks: a survey,” EURASIP Journal on Wireless Communications and Networking , vol. 1, pp. 271-291, 2013.
 W. Stallings, “Cryptography and network security: principles and practices,” Pearson Education India, 2006.
 M. Solanki and D. Vidya, “Intrusion Detection System by using K-Means clustering C 4.5 FNN SVM classifier,” Int. J. Emerg. Trends Technol. Comput, vol. 3, pp. 6-16, 2014.
 V. Kosamkar and S. Sangita, “Improved Intrusion detection system using C4. 5 decision tree and support vector machine,” PhD diss., Doctoral dissertation, Mumbai University, 2013.
 J. Li, Y. Liu, and L. Gu, “DDoS attack detection based on neural network,” In: Aware Computing (ISAC), 2nd International Symposium on. IEEE, 2010.
 A. Balon-Perin and G. Björn, “Ensembles of decision trees for network intrusion detection systems,” International Journal on Advances in Security, 2013.
 D. M. Farid, H. Nouria, and Z. Mohammad, “Combining naive bayes and decision tree for adaptive intrusion detection,” arXiv preprint arXiv:1005.4496, 2010.
 M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” In 2009 IEEE symposium on computational intelligence for security and defense applications, IEEE, 2009.
 J. Cannady, “Artificial neural networks for misuse detection,” In: National information systems security conference, 1998.
 M. S. Hoque, M. Mukit, M. Bikas, and A. Naser, “An implementation of intrusion detection system using genetic algorithm,” arXiv Prepr arXiv12041336, 2012.
 Xu. Xin and W. Xuening, “An adaptive network intrusion detection method based on PCA and support vector machines,” In International Conference on Advanced Data Mining and Applications, pp. 696-703, 2005.
 R. Naoum, A. L. Abdullah, and Sh. Marwan, “A Hybrid Intrusion Detection System Using Hamming and MAXNET Neural Nets Using NDIS Dataset,” Journal of Emerging Trends in Computing and Information Sciences, vol. 4, pp. 198-203, 2013.
 B. C. Rhodes, A. James. Mahaffey, and D. James, “Multiple self-organizing maps for intrusion detection,” In Proceedings of the 23rd national information systems security conference, pp. 16-19, 2000.
 J. Feng, Y. Sui, and C. Cao, “An incremental decision tree algorithm based on rough sets and its application in intrusion detection,” Artificial Intelligence Review 40, vol. 40, pp. 517-530, 2013.
 C. hou, Te. Shun, Kang K. Yen, and L. Jun, “Network intrusion detection design using feature selection of soft computing paradigms,” International journal of computational intelligence, 2008.
 I. Ahmad, A. B. Abdulah, A. S. Alghamdi, K. Alnfajan, and M. Hussain, “Feature subset selection for network intrusion detection mechanism using genetic eigen vectors,” In: Proceedings of 2011 International Conference on Telecommunication Technology and Applications (ICTTA 2011), 2011.##
 F. López, G. T. Miguel, B. Belén, A. Moreno Pérez, and J. Marcos, “Solving feature subset selection problem by a parallel scatter search,” European Journal of Operational Research, vol. 169, pp. 477-489, 2006.##
 J. Yang and H. Vasant, “Feature subset selection using a genetic algorithm,” In Feature extraction, construction and selection, Springer, Boston, MA, pp. 117-136, 1998.##
 H. Nama and A. Seyyed, “Application of data mining techniques to detect computer network penetration,” The first international conference on the new achievements in electrical engineering and computer science, 2010. (In persian)##
 M. J. Asbagh and H. Abolhassani, “Feature-Based Data Stream Clustering,” In: Computer and Information Science, ICIS 2009 Eighth IEEE/ACIS International Conference on. IEEE, 2009.##
 M. Dash, K. Choi, P. Scheuermann, and H. Liu, “Feature selection for clustering-a filter solution,” In: Data Mining, ICDM Proceedings 2002 IEEE International Conference on. IEEE, 2002.##
 M. D. Hasan, M. AlMehedi, N. Mohammed, A. Shamim, and I. Khademul, “Feature selection for intrusion detection using random forest,” Journal of information security, vol.7, pp. 129-140, 2016.##
 A. Das and S. Siva Sathya, “Association Rule Mining For Kdd Intrusion Detection Data Set,” International Journal Of Computer Science And Informatics Issn (PRINT), pp. 2231-5292, 2012.##
 A. Özgür and H. Erdem, “The impact of using large training data set KDD99 on classification accuracy,” Peer J. Prepr., vol. 5, pp. 283-287, 2017.##
 A. Ghadiri and N. Ghadiri, “An adaptive hybrid architecture for intrusion detection based on fuzzy clustering and RBF neural networks,” In: Communication Networks and Services Research Conference (CNSR), Ninth Annual. IEEE, 2011.##
 Gharehchopogh, F. Soleimanian, M. Molany, and F. Dabaghchi Mokri, “Using artificial neural network in diagnosis of thyroid disease: a case study,” International Journal on Computational Sciences & Applications (IJCSA), 2013.##
 Y. Chen, A. Ajith, and Ju. Yang, “Feature selection and intrusion detection using hybrid flexible neural tree,” In International Symposium on Neural Networks, Springer, Berlin, Heidelberg, 2005.##
 Rafiqul, et al., “Classification of malware based on integrated static and dynamic features,” Journal of Network and Computer Applications, vol. 36, no. 2, pp. 646-656, 2013.##
 Z. Amirkhani, M. Madani, M. H. Sadipour, and S. Sadat, “Increasing location accuracy in neural network based wireless communications systems,” Cyber Defense and Cyber Defense, vol. 3, pp. 31-38, 1394.(In persian)##
 N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A Deep Learning Approach to Network Intrusion Detection,” In IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41-50, 2018.##
 K. Siddique, Z. Akhtar, F. Aslam Khan, and Y. Kim, “KDD Cup 99 Data Sets: A Perspective on the Role of Data Sets in Network Intrusion Detection Research,” In Computer, vol. 52, no. 2, pp. 41-51, 2019.##
 O. Rashid, Z. Othman, and S. Zainudin, “Features Selection for Intrusion Detection System Based on DNA Encoding,” In: Intelligent and Interactive Computing, Lecture Notes in Networks and Systems, Springer, vol. 67, 2019.##
 A. K. Ghosh, C. Michael, and M. Schatz, “A real-time intrusion detection system based on learning program behavior,” In: Proceedings of the hird International Workshop on Recent Advances in Intrusion Detection Toulouse, France, 2000.##
 L. Hung-Jen, L. Chun-Hung, L.Ying-Chih, and T. Kuang-Yuan, “Intrusion detection system: A comprehensive review,” Journal of Network and Computer Applications, vol. 36, pp. 16-24, 2013.##
 S. Horng, M. Su, Y. Chen, T. Kao, R. Chen, and J. Lai, “A novel intrusion detection system based on hierarchical clustering and support vector machines,” Expert Syst. Appl., vol. 38, no. 1, pp. 306–313, 2011.##