Novel applications of Machine Learning to Network Traffic Analysis

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features as an image is correct, and therefore CNNs are valid candidates for dealing with vector time-series of similar nature Being the deep learning architectures such a fruitful source of new models, we consider, as future work, to experiment with new applications and variants of the CNN and LSTM models. This work can be especially applicable for new IoT networks in which NTC can be used to differentiate or segregate different classes of traffic, e.g. device identification [36], target detection in Wireless Sensor Networks (WSN) [37] or user priority based [38]. REFERENCES 1. B. Ng, M. Hayes and W. K. G. Seah, "Developing a traffic classification platform for enterprise networks with SDN: Experiences & lessons learned," 2015 IFIP Networking Conference (IFIP Networking), Toulouse, 2015, pp. 1-9. 2. A. Sivanathan, D. Sherrat, H. Habibi Gharakheili, A. Radford, C. Wijenayake, A. Vishwanath, and V. Sivaraman , “Characterizing and Classifying IoT Traffic in Smart Cities and Campuses”, IEEE INFOCOM Workshop on SmartCity: Smart Cities and Urban Computing, USA, May 2017. 3. T. Nguyen and G. Armitage, “A survey of techniques for internet traffic classification using machine learning,” IEEE Commun. Surv. Tutorials, vol. 10, no. 4, pp. 56–76, 2008. 4. J. Zhang, X. Chen, Y. Xiang, W. Zhou, and J. Wu, “Robust Network Traffic Classification,” IEEE/ACM Trans. Netw., vol. 23, no. 4, pp. 1257–1270, 2015. 5. T. Auld, A. W. Moore, and S. F. Gull, “Bayesian Neural Networks for Internet Traffic Classification,” IEEE Trans. Neural Networks, vol. 18, no. 1, pp. 223–239, Jan. 2007. 6. X. Xie, B. Yang, Y. Chen, L. Wang and Z. Chen, "Network Traffic Classification Based on Error-Correcting Output Codes and NN Ensemble," 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, 2009, pp. 475-479. 7. Chen, Z., Wang, H., Abraham, A., Grosan, C., Yang, B., Chen, Y., Wang, L., “Improving Neural Network Classification Using Further Division of Recognition Space”. International Journal of Innovative, Computing, Information and Control 5(2) (2009) 8. Herrero, A., Corchado, E., Gastaldo, P., Zunino, R., “Neural projection techniques for the visual inspection of network traffic”. Neurocomputing, Volume 72, Issue 16, Pages 3649- 3658, 2009. 9. Kothari, A., Keskar, A., “Rough Set Approaches to Unsupervised Neural Network Based Pattern Classifier”, Advances in Machine Learning and Data Analysis, Springer Netherlands, Dordrecht, pp. 151-163, 2010. 10. W. Zhou, L. Dong, L. Bic, M. Zhou and L. Chen, "Internet traffic classification using feed-forward neural network," 2011 International Conference on Computational Problem- Solving (ICCP), Chengdu, 2011, pp. 641-646. 11. A Moore D Zuev L. Crogan "Discriminators for use in flow-based classification", Technical Report RR-05-13. Department of Computer Science Queen's Mary University, 2005. 12. Bereket Mathewos, Marco Carvalho, and Fredric Ham. 2011. “Network traffic classification using a parallel neural network classifier architecture”. In Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research (CSIIRW '11), Frederick T. Sheldon, Robert Abercrombie, and Axel Krings (Eds.). ACM, New York, NY, USA, , Article 33 , 1 pages. 13. Kim, H., Claffy, K., Fomenkov, M., Barman, D., Faloutsos, M., Lee, K.: “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. 14. M. Shafiq, Xiangzhan Yu, A. A. Laghari, Lu Yao, N. K. Karn and F. Abdessamia, "Network Traffic Classification techniques and comparative analysis using Machine Doctoral Thesis: Novel applications of Machine Learning to NTAP - 125

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