Novel applications of Machine Learning to Network Traffic Analysis

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[59] Aggarwal, C.C. Outlier Analysis; Springer: New York, NY, USA, 2013; pp. 10–18, ISBN 978-1-4614-639-5. [60] Hodo E., Bellekens X. and 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. [61] Kang M.J. and Kang J.W., “Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security”. PLoS ONE 2016, 11, e0155781. [62] 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. [63] Ma T. et al., “A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks”. Sensors 2016, 16, 1701. [64] An J. and Cho S., “Variational Autoencoder based Anomaly Detection using Reconstruction Probability”, Seoul National University, SNU Data Mining Center, 2015-2 Special Lecture on IE, Seoul, Korea, 2015. [65] Suh, S., Chae, D.H., Kang, H.G and 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. [66] 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. [67] Tavallaee, M. et al. “A detailed analysis of the KDD CUP 99 data set”. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada, 8–10 July 2009; pp. 1–6. [68] Ingre, B. and Yadav, A. “Performance analysis of NSL-KDD dataset using ANN”. In Proceedings of the 2015 International Conference on Signal Processing and Communication Engineering Systems, Guntur, India, 2–3 January 2015; pp. 92–96, doi:10.1109/SPACES.2015.7058223. [69] Ibrahim, L.M, Basheer, D.T. and Mahmod, M.S. “A comparison study for intrusion database (KDD99, NSL-KDD) based on self-organization map (SOM) artificial neural network”. In Journal of Engineering Science and Technology; School of Engineering, Taylor’s University: Selangor, Malaysia, 2013; Volume 8, pp. 107–119. [70] Wahb, Y, ElSalamouny E. and ElTaweel, G. “Improving the Performance of Multi-class Intrusion Detection Systems using Feature Reduction”. arXiv 2015, arXiv:1507.06692. [71] Chen, C.-M et al.,”Anomaly Network Intrusion Detection Using Hidden Markov Model”. Int. J. Innov. Comput. Inform. Control 2016, 12, 569–580. [72] Alom, M.Z., Bontupalli, V. and Taha, T.M. “Intrusion detection using deep belief networks”. In Proceedings of the 2015 National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA, 15–19 June 2015, pp. 339–344. [73] Xu, J. and Shelton, C.R. “Intrusion Detection using Continuous Time Bayesian Networks”. J. Artif. Intell. Res. 2010, 39, 745–77. [74] Hodo, E. et al., “Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey”. arXiv 2017, arXiv:1701.02145. [75] Abolhasanzadeh B., “Nonlinear dimensionality reduction for intrusion detection using auto-encoder bottleneck features,” in 2015 7th Conference on Information and Knowledge Technology (IKT), 2015, pp. 1–5. [76] Fiore U. et al., “Network anomaly detection with the restricted Boltzmann machine,” Neurocomputing, vol. 122, pp. 13–23, Dec. 2013. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 89

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