M. S. Abadeh, H. Mohamadi and J. Habibi, Design and analysis of genetic fuzzy systems for intrusion detection in computer networks, Expert Syst. Appl., 38 (2011), 7067-7075.
 B. Agarwal and N. Mittal, Hybrid approach for detection of anomaly network traffic using data mining techniques, Proceedings of ICCCS, (2012), Procedia Technology, 6 (2012), 996-1003.
 R. Ashok, A. Lakshmi, G. V. Rani and M. N. Kumar, Optimized feature selection with k-means clustered triangle SVM for intrusion detection, Proceedings of ICOAC, (2011), 23-27.
 E. Atashpaz-Gargari and C. Lucas, Imperialist Competitive algorithm: An algorithm for optimization inspired by imperialist competition, Proceedings of the CEC, (2007), 4661-4667.
 A. A. Aziz, S. E. Hanafi and A. E. Hassanien, Comparison of classification techniques applied for network intrusion detection and classification, J. Appl. Logic, 24 (2017), 109–118.
 M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, Network anomaly detection: methods, systems and tools, IEEE Commun. Surv. Tutor., 16(1) (2014), 303-336.
 E. Corchado and A. Herrero, Neural visualization of network traffic data for intrusion detection, Appl. Soft. Comput., 11 (2011), 2042-2056.
 T. Dash, A Study on Intrusion Detection Using Neural Networks Trained with Evolutionary Algorithms, soft comput., 21(10), (2017), 2687-2700.
 S. Devaraju and S. Ramakrishnan, Detection of accuracy for intrusion detection system using neural network classifier, Int. J. Emerging Technol. Adv. Eng., 3 (2013), 2250-2459.
 R. Devi, R. K. Jha, A. Gupta, S. Jain and P. Kumar, Implementation of intrusion detection system using adaptive neuro-fuzzy inference system for 5G wireless communication network, INT. J. ELECTRON. C., 74(2017), 94-106.
 W. Feng, Q. Zhang, G. Hu and J. X. Huang, Mining network data for intrusion detection through combining SVMs with ant colony networks, Future Gener. Comput. Syst., 37 (2014), 127–140.
 F. Haddadi, S. khanchi, M. Shetabi and V. Derhami, Intrusion detection and attack classification using feed-forward neural network, Proceedings of ICCNT, (2010), 262-266.
 R. G. Helali, Data mining based network intrusion detection system: A survey, in: Novel Algorithms and Techniques in Telecommunications and Networking, Springer Science+Business, (2010).
 M. Kuchaki Rafsanjani and M. Samareh, Chaotic time series prediction by artificial neural networks, J. Comput. Meth. Sci. Eng., 16 (2016), 599-615.
 M. Kuchaki Rafsanjani and Z. A. Varzaneh, Intrusion detection by data mining algorithms: a review, J. New Resul. Sci., 2 (2013), 76-91.
 S. A. Mulay, P. R. Devale and G. V. Garje, Decision tree based support vector machine for intrusion detection, Proceedings of ICNIT, (2010), 59–63.
 S. Pilabutr, P. Somwang and S. Srinoy, Integrated soft computing for intrusion detection on computer network security, Proceedings of ICCAIE, (2011), 559-563.
 A. Rapaka, A. Novokhodko and D. Wunsch, Intrusion detection using radial basis function network on sequence of system calls, Proceedings of IJCNN03, (2003), 1820-1825.
 Z. Salek, F. M. Madani R. azmi, Intrusion detection using Neural Networks trained by differential evaluation algorithm, Proceedings of ISCISC, (2013).
 P. Sangkatsanee, N. Wattanapongsakorn and C. Charnsripinyo, Practical real-time intrusion detection using machine learning approaches, Comput. Commun., 34 (2011), 2227-2235.
 S. S. Sivatha Sindhu, S. Geetha and A. Kannan, Decision tree based light weight intrusion detection using a wrapper approach, Expert Systems with Applications, 39 (2012), 129-141.
 W. J. Tian and J. C. Liu, Network intrusion detection analysis with neural network and particle swarm optimization algorithm, Proceedings of the CCDC, (2010), 1749-1752.
 G. Wang, J. Hao, J. Ma and L. Huang, A New approach to intrusion detection using artificial neural networks and fuzzy clustering, Expert Syst. Appl., 37 (2010), 6225-6232.
 Sh. X. Wu and W. Banzhaf, The use of computational intelligence in intrusion detection systems: a review, Appl. Soft Comput., 10 (2010), 1-35.
 C. Zhang, J. Jiang and M. Kamel, Intrusion detection using hierarchical neural networks, Pattern Recognit. Lett., 26 (2005), 779-791.