Novel applications of Machine Learning to Network Traffic Analysis

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[40] Bilal K. and Erbad A., "Edge computing for interactive media and video streaming," 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), Valencia, Spain, May 8-11, 2017, pp. 68-73 [41] Ananthanarayanan G. et al., "Real-Time Video Analytics: The Killer App for Edge Computing," Computer, vol. 50, no. 10, 2017, pp. 58-67. [42] Chen Y., Wu K. and Zhang Q., "From QoS to QoE: A Tutorial on Video Quality Assessment," IEEE Communications Surveys & Tutorials, vol. 17, no. 2, 2015, pp. 1126- 1165. [43] Huang F. et al., “Reliability Evaluation of Wireless Sensor Networks Using Logistic Regression”, 2010 International Conference on Communications and Mobile Computing, Shenzhen (April 2010), Vol. 3, Pages 334-338 [44] Feng Het al., “SVM-Based Models for Predicting WLAN Traffic”, 2006 IEEE International Conference on Communications (ICC 2006), Istanbul (June 2006), Vol. 2, Pages 597-602 [45] Meidan Y. et al., ‘‘ProfilIoT: A machine learning approach for IoT device identification based on network traffic analysis,’’ in Proc. ACM Symp. Appl. Comput. (SAC), New York, NY, USA, 2017, pp. 506–509. [46] Althunibat S. et al., ‘‘Countering intelligent-dependent malicious nodes in target detection wireless sensor networks,’’ IEEE Sensors J., vol. 16, no. 23, pp. 8627–8639, Dec. 2016. [47] Grajzer M. et al., ‘‘A multi- classification approach for the detection and identification of eHealth applications,’’ in Proc. 21st Int. Conf. Comput. Commun. Netw. (ICCCN), Munich, Germany, Jul./Aug. 2012, pp. 1–6. [48] Bhuyan, M.H., Bhattacharyya, D.K. and Kalita, J.K. “Network Anomaly Detection: Methods, Systems and Tools”. In IEEE Communications Surveys & Tutorials; IEEE: Piscataway, NJ, USA, 2014; Volume 16, pp. 303–336. [49] Vacca J. R., “Computer and Information Security Handbook (Second Edition)”, Morgan Kaufmann, 2013, Pages 81-95. https://doi.org/10.1016/B978-0-12-394397-2.00005-2 [50] Kruegel C. et al., “Intrusion detection and correlation - Challenges and Solutions”, Part of the Advances in Information Security book series (ADIS, volume 14). 2005. DOI: 10.1007/B101493 [51] Kumar D.A. and Venugopalan S.R., “Intrusion detection systems: A review”, International Journal of Advanced Research in Computer Science, vol 8, no. 8, 2017. DOI: http://dx.doi.org/10.26483/ijarcs.v8i8.4703 [52] Marty R., The Security Data Lake, O’Reilly Media, Inc., 2015, https://learning.oreilly.com/library/view/the-securitydata/9781491927748/ [53] Marty R., “AI & ML IN CYBERSECURITY, Why Algorithms Are Dangerous”, BlackHat, USA, August 2018. https://i.blackhat.com/us-18/Thu-August-9/us-18-Marty- AI-and-ML-in-Cybersecurity.pdf [54] Gao D., Reiter M.K. and Song D., “Behavioral distance for intrusion detection”. In Proceedings of the 8th international conference on Recent Advances in Intrusion Detection (RAID'05), Alfonso Valdes and Diego Zamboni (Eds.). Springer-Verlag, Berlin, Heidelberg, 63-81. 2005. DOI=http://dx.doi.org/10.1007/11663812_4 [55] Song Y., Keromytis A. D. and Stolfo S., “Spectrogram: A Mixture-of-Markov-Chains Model for Anomaly Detection in Web Traffic”, Network and Distributed System Security Symposium 2009: February 8-11, San Diego, California. 2009. https://doi.org/10.7916/D8891G6G [56] Marty R., “Applied Security Visualization (1 ed.)”. Addison-Wesley Professional. 2008. [57] Sharafaldin I. et al., “An Evaluation Framework For Network Security Visualizations”. Computers & Security, 2019. https://doi.org/10.1016/j.cose.2019.03.005 . [58] Chuvakin A. A. et al., “Logging and Log Management”. Syngress, Elsevier. Book. 2013 Doctoral Thesis: Novel applications of Machine Learning to NTAP - 88

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