Novel applications of Machine Learning to Network Traffic Analysis

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[95] Nguyen T. and Armitage G., “A survey of techniques for internet traffic classification using machine learning,” IEEE Commun. Surv. Tutorials, vol. 10, no. 4, pp. 56–76, 2008. [96] Zhang J. et al., “Robust Network Traffic Classification,” IEEE/ACM Trans. Netw., vol. 23, no. 4, pp. 1257–1270, 2015. [97] Auld T., Moore A. W. and Gull S. F., ‘‘Bayesian neural networks for Internet traffic classification,’’ IEEE Trans. Neural Netw., vol. 18, no. 1, pp. 223–239, Jan. 2007. [98] Xie X. et al., ‘‘Network traffic classification based on error-correcting output codes and NN ensemble,’’ in Proc. 6th Int. Conf. Fuzzy Syst. Knowl. Discovery, Tianjin, China, Aug. 2009, pp. 475–479. [99] Chen Z. et al., ‘‘Improving neural network classification using further division of recognition space,’’ Int. J. Innov., Comput., Inf. Control, vol. 5, no. 2, pp. 301–310, 2009. [100] Zhou W. et al., ‘‘Internet traffic classi- fication using feed-forward neural network,’’ in Proc. Int. Conf. Comput. Problem-Solving (ICCP), Chengdu, China, Oct. 2011, pp. 641– 646. [101] Mathewos B., Carvalho M. and Ham F., ‘‘Network traffic classification using a parallel neural network classifier architecture,’’ in Proc. 7th Annu. Workshop Cyber Secur. Inf. Intell. Res. (CSIIRW), New York, NY, USA, 2011, p. 33. [102] Moore A. W. and Papagiannaki D., “Toward the accurate identification of network applications,” in Proc. 6th Passive Active Meas. Workshop (PAM), Mar. 2005, vol. 3431, pp. 41–54. [103] Zhou W. et al., "Internet traffic classification using feed-forward neural network," 2011 International Conference on Computational Problem-Solving (ICCP), Chengdu, 2011, pp. 641-646. [104] Moore A., Zuev D. and Crogan L., "Discriminators for use in flow-based classification", Technical Report RR-05-13. Department of Computer Science Queen’s Mary University, 2005. [105] Kim, H. et al., “Internet traffic classification demystified: myths, caveats, and the best practices”, In Proceedings of the 2008 ACM CoNEXT Conference (CoNEXT '08). ACM, New York, NY, USA, pp. 11:1–11:12, 2008. [106] Shafiq M. et al., "Network Traffic Classification techniques and comparative analysis using Machine Learning algorithms," 2016 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, 2016, pp. 2451-2455. [107] Wang C., Xu T. and Qin X., "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 [108] Zhang J. et al. “An Effective Network Traffic Classification Method with Unknown Flow Detection”, IEEE Transaction on Network and ServiceManagement, Vol 12, Dec 2013 [109] Hao S. et al., "Network traffic classification based on improved DAG-SVM," 2015 International Conference on Communications, Management and Telecommunications (ComManTel), DaNang, 2015, pp.256-26 [110] Yamansavascilar B et al., "Application identification via network traffic classification," 2017 International Conference on Computing, Networking and Communications (ICNC), Santa Clara, CA, 2017, pp. 843-848. [111] Canadian Institute for Cybersecurity, University of New Brunswick, Datasets, web: “http://www.unb.ca/research/iscx/dataset/ISCX-network-traffic-VPNdataset “, html as of Nov 29, 2018 [112] Yuan Z. and Wang C., "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. doi: 10.1109/ICOACS.2016.7563047 Doctoral Thesis: Novel applications of Machine Learning to NTAP - 91

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