Novel applications of Machine Learning to Network Traffic Analysis

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Learning algorithms," 2016 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, 2016, pp. 2451-2455. 15. C. Wang, T. Xu and X. Qin, "Network Traffic Classification with Improved Random Forest," 2015 11th International Conference on Computational Intelligence and Security (CIS), Shenzhen, 2015, pp. 78-81. doi: 10.1109/CIS.2015.27 16. Jun Zhang, Chao Chen, Yang Xiang, Wanlei Zhou, and Athanasios V. Vasilakos, “An Effective Network Traffic Classification Method with Unknown Flow Detection”, IEEE Transaction on Network and ServiceManagement, Vol 12, Dec 2013 17. Y.-s. Lim, H.-c. Kim, J. Jeong, C.-k. Kim, T. T. Kwon, and Y. Choi, “Internet traffic classification demystified: on the sources of the discriminative power,” In Proceedings of the 6th International COnference (Co-NEXT '10). ACM, New York, NY, USA, pp. 9:1– 9:12. 2010. 18. J. Erman, A. Mahanti, M. Arlitt, I. Cohen, and C. Williamson, “Offline/realtime traffic classification using semi-supervised learning,” Performance Evaluation, vol. 64, no. 9-12, pp. 1194–1213, Oct. 2007. 19. N. Williams, S. Zander, and G. Armitage, “A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification,” SIGCOMM Comput. Commun. Rev., vol. 36, pp. 5–16, Oct. 2006. 20. M. Roughan, S. Sen, O. Spatscheck, and N. Duffield, “Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification,” in Proc. 2004 ACM SIGCOMM Conference on Internet Measurement, pp. 135–148. 21. A. W. Moore and D. Zuev, “Internet traffic classification using Bayesian analysis techniques,” SIGMETRICS Perform. Eval. Rev., vol. 33, pp. 50–60, June 2005. 22. S. Hao, J. Hu, S. Liu, T. Song, J. Guo and S. Liu, "Network traffic classification based on improved DAG-SVM," 2015 International Conference on Communications, Management and Telecommunications (ComManTel), DaNang, 2015, pp.256-26 23. B. Yamansavascilar, M. A. Guvensan, A. G. Yavuz and M. E. Karsligil, "Application identification via network traffic classification," 2017 International Conference on Computing, Networking and Communications (ICNC), Santa Clara, CA, 2017, pp. 843- 848. 24. Z. Yuan and C. Wang, "An improved network traffic classification algorithm based on Hadoop decision tree," 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), Chongqing, 2016, pp. 53-56. 25. doi: 10.1109/ICOACS.2016.7563047 26. L. Deri, M. Martinelli, T. Bujlow, and A. Cardigliano, “nDPI: Open-source high-speed deep packet inspection,” in 2014 International Wireless Communications and Mobile Computing Conference (IWCMC), 2014, pp. 617–622. 27. T. Bujlow, V. Carela-Español, and P. Barlet-Ros, “Independent comparison of popular DPI tools for traffic classification,” Comput. Networks, vol. 76, pp. 75–89, 2015. 28. Behnke, Sven, “Hierarchical Neural Networks for Image Interpretation”, Volume 2766 of Lecture Notes in Computer Science, Springer-Verlag, 2003 29. Zachary C. Lipton, John Berkowitz, Charles Elkan (2015), “A Critical Review of Recurrent Neural Networks for Sequence Learning”, arXiv:1506.00019 [cs.LG] 30. Klaus Greff; Rupesh Kumar Srivastava; Jan Koutník; Bas R. Steunebrink; Jürgen Schmidhuber (2015). "LSTM: A Search Space Odyssey". arXiv:1503.04069 31. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. “Dropout: a simple way to prevent neural networks from overfitting”. J. Mach. Learn. Res. 15, 1 (January 2014), 1929-1958. 32. Chen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu (2015), “Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree”, arXiv:1509.08985 [stat.ML] Doctoral Thesis: Novel applications of Machine Learning to NTAP - 126

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