Novel applications of Machine Learning to Network Traffic Analysis

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12. [1] [2] [3] [4] [5] LIST OF REFERENCES Lopez-Martin M, Sanchez-Esguevillas A. and Carro B., “Review of methods to predict connectivity of IoT wireless devices”, Ad Hoc & Sensor Wireless Networks, 38.1-4, p. 125-141. http://www.oldcitypublishing.com/journals/ahswn-home/ahswn-issue- contents/ahswn-volume-38-number-1-4-2017/ahswn-38-1-4-p-125-141/ Lopez-Martin M et al., “Network traffic classifier with convolutional and recurrent neural networks for Internet of Things”, IEEE Access, vol. 5, pp. 18042-18050, 2017. doi: https://doi.org/10.1109/ACCESS.2017.2747560 Lopez-Martin M et al., “Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT”. Sensors 17 (9), 2017. doi: https://doi.org/10.3390/s17091967 Lopez-Martin M et al., “Deep learning model for multimedia Quality of Experience prediction based on network flow packets”, IEEE Communications Magazine, September 2018. doi: https://doi.org/10.1109/MCOM.2018.1701156 Lopez-Martin M, Carro B. and Sanchez-Esguevillas A., “Variational data generative model for intrusion detection”, Knowledge and Information Systems, December-2018. doi: https://doi.org/10.1007/s10115-018-1306-7 [6]Wang M. et al., "Machine Learning for Networking: Workflow, Advances and Opportunities," in IEEE Network, vol. 32, no. 2, pp. 92-99, March-April 2018. [7] Mahdavinejad M.S. et al., “Machine learning for Internet of Things data analysis: A survey”, Digital Communications and Networks, 2017. [8] Boutaba R. et al. “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities”, Journal of Internet Services and Applications (2018) 9:16, https://doi.org/10.1186/s13174-018-0087-2 [9] Fadlullah Z. M. et al., "State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems," in IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2432-2455, 2017. [10] Jiang C. et al., "Machine Learning Paradigms for Next-Generation Wireless Networks," in IEEE Wireless Communications, vol. 24, no. 2, pp. 98-105, April 2017 [11] Breiman L., “Random Forests. Machine Learning”, Volume 45, Issue 1, October 1 2001, Pages 5-32 [12] Friedman J.H., “Greedy Function Approximation: A Gradient Boosting Machine”. The Annals of Statistics, Vol. 29, No. 5 (Oct., 2001), pp. 1189-1232 [13] Cortes, C., Vapnik, V., "Support-vector networks". Machine Learning. 20 (3): 273–297. 1995. [14] Hastie T., Tibshirani R. and Friedman J., “The Elements of Statistical Learning: Data mining, inference, and prediction”. Second Edition (2009), Springer Series in Statistics [15] Haykin, S.,“Neural Networks: A Comprehensive Foundation”. 1998, Prentice Hall. [16] Bishop C.M., “Pattern Recognition and Machine Learning” (Information Science and Statistics). 2006, Springer-Verlag New York, Inc., Secaucus, NJ, USA. [17] Chapelle, O., Schölkopf, B. and Zien, A., “Semi-supervised learning”. Cambridge, 2006, Mass.MIT Press [18] Sutton R.S and Barto A.G., “Reinforcement Learning”, Cambridge, 1998, Mass.MIT Press [19] Goodfellow I., Bengio Y. and Courville A., “Deep Learning”, Cambridge, 2016, Mass.MIT Press [20] Behnke S., “Hierarchical Neural Networks for Image Interpretation” (Lecture Notes in Computer Science), vol. 2766. Berlin, Germany: Springer-Verlag, 2003. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 86

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