Novel applications of Machine Learning to Network Traffic Analysis

PDF Publication Title:

Novel applications of Machine Learning to Network Traffic Analysis ( novel-applications-machine-learning-network-traffic-analysis )

Previous Page View | Next Page View | Return to Search List

Text from PDF Page: 147

provided background information on related experiments in IoT and co-guided the course of research. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. References [1] Zarpelo, B.B.; Miani, R.S.; Kawakani, C.T.; de Alvarenga, S.C. A survey of intrusion detection in Internet of Things. J. Netw. Comput. Appl. 2017, 84, 25–37, doi:10.1016/j.jnca.2017.02.009. [2] Bhuyan, M.H.; Bhattacharyya, D.K.; 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, doi:10.1109/SURV.2013.052213.00046. [3] Aggarwal, C.C. Outlier Analysis; Springer: New York, NY, USA, 2013; pp. 10–18, ISBN 978-1-4614-639-5. [4] Kingma, D.P.; Rezende, D.J.; Mohamed, S.; Welling, M. Semi-supervised learning with deep generative models. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14), Montreal, QC, Canada, 8–13 December 2014, Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q., Eds.; MIT Press: Cambridge, MA, USA, 2014; pp. 3581–3589. [5] Sohn, K.; Yan, X.; Lee, H. Learning structured output representation using deep conditional generative models. In Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS’15), Montreal, QC, Canada, 7–12 December 2015, Cortes, C., Lee, D.D., Sugiyama, M., Garnett, R., Eds.; MIT Press: Cambridge, MA, USA, 2015; pp. 3483–3491. [6] Fekade, B.; Maksymyuk, T.; Kyryk, M.; Jo, M. Probabilistic Recovery of Incomplete Sensed Data in IoT. IEEE Int. Things J. 2017, 1, doi:10.1109/JIOT.2017.2730360. [7] An, J.; Cho, S. Variational Autoencoder based Anomaly Detection using Reconstruction Probability. Seoul National University, Seoul, Korea, SNU Data Mining Center, 2015–2 Special Lecture on IE, 2015. [8] Suh, S.; Chae, D.H.; Kang, H.G.; Choi, S. Echo-state conditional Variational Autoencoder for anomaly detection. In Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 24–29 July 2016, pp. 1015–1022, doi:10.1109/IJCNN.2016.7727309. [9] Sölch, M. Detecting Anomalies in Robot Time Series Data Using Stochastic Recurrent Networks. Master’s Thesis, Department of Mathematics, Technische Universitat Munchen, Munich, Germany, 2015. [10] Hodo, E.; Bellekens, X.; Hamilton, A. Threat analysis of IoT networks using artificial neural network intrusion detection system. In Proceedings of the 2016 International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet, Tunisia, 11–13 May 2016; pp. 1–6, doi:10.1109/ISNCC.2016.7746067. [11] Kang, M.-J.; Kang, J.-W. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security. PLoS ONE 2016, 11, e0155781, doi:10.1371/journal.pone.0155781. [12] Thing, V.L.L. IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach. In Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, 19–22 March 2017, pp. 1–6, doi:10.1109/WCNC.2017.7925567. [13] Ma, T.; Wang, F.; Cheng, J.; Yu, Y.; Chen, X. A Hybrid Spectral Clustering and Deep Doctoral Thesis: Novel applications of Machine Learning to NTAP - 145

PDF Image | Novel applications of Machine Learning to Network Traffic Analysis

novel-applications-machine-learning-network-traffic-analysis-147

PDF Search Title:

Novel applications of Machine Learning to Network Traffic Analysis

Original File Name Searched:

456453_1175348.pdf

DIY PDF Search: Google It | Yahoo | Bing

Cruise Ship Reviews | Luxury Resort | Jet | Yacht | and Travel Tech More Info

Cruising Review Topics and Articles More Info

Software based on Filemaker for the travel industry More Info

The Burgenstock Resort: Reviews on CruisingReview website... More Info

Resort Reviews: World Class resorts... More Info

The Riffelalp Resort: Reviews on CruisingReview website... More Info

CONTACT TEL: 608-238-6001 Email: greg@cruisingreview.com | RSS | AMP