Novel applications of Machine Learning to Network Traffic Analysis

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[77] Gao N. et al., “An Intrusion Detection Model Based on Deep Belief Networks,” in 2014 Second International Conference on Advanced Cloud and Big Data, 2014, pp. 247–252. [78] Panda M., et al., “Discriminative Multinomial Naïve Bayes for Network Intrusion Detection”, Proceedings of 6th Intl. conf. on information assurance and security (IAS- 2010), Aug. 2010, USA, 5-10, IEEE Press, 2010 [79] Dhanabal , L and Shantharajah, S.P. “A Study on NSL- KDD Dataset for Intrusion Detection System Based on Classification Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 6 , June 2015, 2015 [80] Kamel S.O.M. et al, “AdaBoost Ensemble Learning Technique for Optimal Feature Subset Selection”, International Journal of Computer Networks and Communications Security VOL. 4, NO. 1, January 2016, 1–11, 2016 [81] Patil D.R and. Pattewar T.M, “A Comparative Performance Evaluation of Machine Learning-Based NIDS on Benchmark Datasets”, International Journal of Research in Advent Technology, Vol.2, No.2, April 2014 E-ISSN: 2321-9637, 2014 [82] Gao H., Wang X. and Yang H., "LS-SVM Based Intrusion Detection using Kernel Space Approximation and Kernel-Target Alignment," 2006 6th World Congress on Intelligent Control and Automation, Dalian, 2006, pp. 4214-4218. [83] Movahedi P. et al. “Fast regularized least squares and k-means clustering method for intrusion detection systems”. 2015. Proceedings of the International Conference on Pattern Recognition Applications and Methods. [84] Joshi M.R. and Hadi T.H., “A Review of Network Traffic Analysis and Prediction Techniques”, 2015, arXiv:1507.05722 [cs.NI] [85] Priyamvada and Wadhvani R. "Review on various models for time series forecasting," 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, 2017, pp. 405-410. [86] Mahalakshmi G., Sridevi S. and Rajaram S., "A survey on forecasting of time series data," 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16), Kovilpatti, 2016, pp. 1-8. [87] Stolojescu-Crisan C., “Data mining based wireless network traffic forecasting”, 2012 10th International Symposium on Electronics and Telecommunications (ISETC), Timisoara (Nov 2012), Pages 115-118. [88] Papadopouli M., “Evaluation of short term traffic forecasting algorithms in wireless networks”, 2006 2nd Conference on Next Generation Internet Design and Engineering, NGI, Valencia (April 2006), Pages 102-109 [89] Shu Y. et al, “Wireless traffic modeling and prediction using seasonal ARIMA models”, IEEE International Conference on Communications, 2003 (ICC 2003), Anchorage (May 2003), Vol. 3, Pages 1675-1679 [90] Huang C.W., Chiang C.T. and Li Q., “A Study of Deep Learning Networks on Mobile Traffic Forecasting,”, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2017. [91] Wang J. et al., "Spatiotemporal modelling and prediction in cellular networks: A big data enabled deep learning approach," IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, Atlanta, GA, 2017, pp. 1-9. [92] Vinayakumar R., Soman K. P. and Poornachandran P., "Applying deep learning approaches for network traffic prediction," 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, 2017, pp. 2353-2358. [93] Feng H.et al, “SVM-Based Models for Predicting WLAN Traffic”, 2006 IEEE International Conference on Communications (ICC 2006), Istanbul (June 2006), Vol. 2, Pages 597-602 [94] Z. Chen, J. Wen, and Y. Geng, “Predicting Future Traffic Using Hidden Markov Models,” Proc. IEEE 24th Int’l. Conf. Network Protocols (ICNP) 2016, pp. 1–6. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 90

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