Novel applications of Machine Learning to Network Traffic Analysis

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[21] Lipton Z. C., Berkowitz J. and Elkan C., “A critical review of recurrent neural networks for sequence learning.’’. 2015, arXiv:1506.00019 [cs.LG] [22] Greff K. et al., ‘‘LSTM: A search space odyssey.’’. 2015, arXiv:1503.04069 [cs.NE] [23] Kingma, D.P. and Welling, M. “Auto-Encoding Variational Bayes”., 2014, arXiv:1312.6114v10. [24] Kingma, D.P. et al., “Semi-supervised learning with deep generative models”. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14), Montreal, QC, Canada, 8–13 December 2014, MIT Press: Cambridge, MA, USA, 2014; pp. 3581–3589. [25] Sohn K., Yan X. and Lee H., “Learning structured output representation using deep conditional generative models”. In Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS’15), Montreal, QC, Canada, 7–12 December 2015, MIT Press: Cambridge, MA, USA, 2015; pp. 3483–3491. [26] Rabiner L. R., “A tutorial on Hidden Markov Models and selected applications in speech recognition”. Proceedings of the IEEE, Vol. 77, No. 2. (06 February 1989), Pages. 257-286 [27] Holt C.C., “Forecasting Trends and Seasonal by Exponentially Weighted Averages”. International Journal of Forecasting, Volume 20, Issue 1 (January–March 2004), Pages 5– 10. [28] Xie M et al., “A seasonal ARIMA model with exogenous variables for Elspot electricity prices in Sweden”, 2013 10th International Conference on the European Energy Market (EEM), Stockholm (May 2013), Pages 1-4 [29] Zhang C., Patras P. and Haddadi H., "Deep Learning in Mobile and Wireless Networking: A Survey," inIEEE Communications Surveys & Tutorials. 2019. doi: 10.1109/COMST.2019.2904897 [30] Kim K. and Aminanto M. E., "Deep learning in intrusion detection perspective: Overview and further challenges,"2017 International Workshop on Big Data and Information Security (IWBIS), Jakarta, 2017, pp. 5-10. doi: 10.1109/IWBIS.2017.8275095 [31] Papernot N. and McDaniel P., “Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning”. arXiv:1803.04765v1 [cs.LG] 13 Mar 2018. [32] Anderson J. W. et al., "Synthetic data generation for the internet of things," 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, 2014, pp. 171-176. [33] OpenAI Blog, (June 2016), web: “https://blog.openai.com/generative-models/ “. html, as of Nov 29, 2018 [34] Zarpelo, B.B. et al., “A survey of intrusion detection in Internet of Things”. J. Netw. Comput. Appl. 2017, 84, 25–37. [35] 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 [36] Yantai S. 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 [37] 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. [38] Sivanathan A et al., ‘‘Characterizing and classifying IoT traffic in smart cities and campuses,’’ in Proc. IEEE INFOCOM Workshop SmartCity, Smart Cities Urban Comput., Atlanta, GA, USA, May 2017, pp. 1–6. [39] Wang J., Pan J., and Esposito F., “Elastic urban video surveillance system using edge computing,”, Proceedings of the Workshop on Smart Internet of Things (SmartIoT '17). ACM, New York, NY, USA, 2017, Article 7 Doctoral Thesis: Novel applications of Machine Learning to NTAP - 87

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