References

Ashfaq, R.A.R., X.Z. Wang, J.Z. Huang, H. Abbas and Y.L. He, 2017. Fuzziness based semi-supervised learning approach for intrusion detection system. Inf. Sci., 378: 484-497.
CrossRef  |  Direct Link  |  

Bhuyan, M.H., D.K. Bhattacharyya and J.K. Kalita, 2014. Network anomaly detection: methods, systems and tools. IEEE. Commun. Surv. Tutorials, 16: 303-336.
CrossRef  |  Direct Link  |  

Buczak, A.L. and E. Guven, 2016. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE. Commun. Surv. Tutorials, 18: 1153-1176.
CrossRef  |  Direct Link  |  

Dhakar, M. and A. Tiwari, 2014. A novel data mining based hybrid intrusion detection framework. J. Inf. Comput. Sci., 9: 037-048.
Direct Link  |  

Dhanabal, L. and S.P. Shantharajah, 2015. A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. Intl. J. Adv. Res. Comput. Commun. Eng., 4: 446-452.
Direct Link  |  

Duque, S. and M.N. Bin Omar, 2015. Using data mining algorithms for developing a model for Intrusion Detection System (IDS). Procedia Comput. Sci., 61: 46-51.
CrossRef  |  Direct Link  |  

Elhag, S., A. Fernandez, A. Bawakid, S. Alshomrani and F. Herrera, 2015. On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Syst. Appl., 42: 193-202.
CrossRef  |  Direct Link  |  

Ha, T., S. Kim, N. An, J. Narantuya and C. Jeong et al., 2016. Suspicious traffic sampling for intrusion detection in software-defined networks. Comput. Networks, 109: 172-182.
CrossRef  |  Direct Link  |  

Hubballi, N. and V. Suryanarayanan, 2014. False alarm minimization techniques in signature-based intrusion detection systems: A survey. Comput. Commun., 49: 1-17.
CrossRef  |  Direct Link  |  

Jabez, J. and B. Muthukumar, 2015. Intrusion Detection System (IDS): Anomaly detection using outlier detection approach. Procedia Comput. Sci., 48: 338-346.
Direct Link  |  

Kaur, R. and S. Singh, 2016. A survey of data mining and social network analysis based anomaly detection techniques. Egypt. Inf. J., 17: 199-216.
Direct Link  |  

Kenkre, P.S., A. Pai and L. Colaco, 2014. Real time intrusion detection and prevention system. Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, November 14-15, 2014, Springer, Cham, Switzerland, ISBN:978-3-319-11932-8, pp: 405-411.

Kim, G., S. Lee and S. Kim, 2014. A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Syst. Appl., 41: 1690-1700.
CrossRef  |  Direct Link  |  

Kumar, G.R., N. Mangathayaru and G. Narsimha, 2016. A novel similarity measure for intrusion detection using Gaussian function. Rev. Tec. Ing. Univ. Zulia., 39: 173-183.
Direct Link  |  

Leu, F.Y., K.L. Tsai, Y.T. Hsiao and C.T. Yang, 2017. An internal intrusion detection and protection system by using data mining and forensic techniques. IEEE. Syst. J., 11: 427-438.
CrossRef  |  Direct Link  |  

Li, W., W. Meng, X. Luo and L.F. Kwok, 2016. MVPSys: Toward practical multi-view based false alarm reduction system in network intrusion detection. Comput. Secur., 60: 177-192.
CrossRef  |  Direct Link  |  

Lin, W.C., S.W. Ke and C.F. Tsai, 2015. CANN: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowl. Based Syst., 78: 13-21.
Direct Link  |  

Nadiammai, G.V. and M. Hemalatha, 2014. Effective approach toward intrusion detection system using data mining techniques. Egypt. Inf. J., 15: 37-50.
CrossRef  |  Direct Link  |  

Naik, P.P. and S.J. Prashsntha, 2014. An approach for building intrusion detection system by using data mining techniques. Intl. J. Emerging Eng. Res. Technol., 2: 112-118.
Direct Link  |  

Ni, X., D. He and F. Ahmad, 2016. Practical network anomaly detection using data mining techniques. VFAST. Trans. Software Eng., 9: 1-6.
CrossRef  |  Direct Link  |  

Patel, J. and K. Panchal, 2015. Effective intrusion detection system using data mining technique. J. Emerging Technol. Innovative Res., 2: 1869-1876.
Direct Link  |  

Posenato, D., F. Lanata, D. Inaudi and I.F. Smith, 2008. Model-free data interpretation for continuous monitoring of complex structures. Adv. Eng. Inf., 22: 135-144.
CrossRef  |  Direct Link  |  

Ravale, U., N. Marathe and P. Padiya, 2015. Feature selection based hybrid anomaly intrusion detection system using K means and RBF kernel function. Procedia Comput. Sci., 45: 428-435.
CrossRef  |  Direct Link  |  

Saurabh, P. and B. Verma, 2016. An efficient proactive artificial immune system based anomaly detection and prevention system. Expert Syst. Appl., 60: 311-320.
CrossRef  |  Direct Link  |  

Subaira, A.S. and P. Anitha, 2014. Efficient classification mechanism for network intrusion detection system based on data mining techniques: A survey. Proceedings of the 2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO), January 10-11, 2014, IEEE, Coimbatore, India, ISBN:978-1-4799-3837-7, pp: 274-280.

Wu, D. and S. Shan, 2015. Meta-analysis of network information security and Web data mining techniques. Proceedings of the 1st International Conference on Information Sciences, Machinery, Materials and Energy, July 7-18, 2015, Atlantis Press, Paris, France, pp: 1974-1977.

Xie, Y., D. Feng, Z. Tan and J. Zhou, 2016. Unifying intrusion detection and forensic analysis via provenance awareness. Future Gener. Comput. Syst., 61: 26-36.
CrossRef  |  Direct Link  |  

Zhao, Y., 2016. Network intrusion detection system model based on data mining. Proceedings of the 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), May 30-June 1, 2016, IEEE, Shanghai, China, ISBN:978-1-5090-0804-9, pp: 155-160.