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INDICE I. RESUMEN ........................................................................................................................................ 4 II. THESIS .......................................................................................................................................... 6 1. 2. 3. RESEARCH OBJECTIVES............................................................................................................ 6 THESIS FRAMEWORK ............................................................................................................... 10 RESEARCH CONTEXT AND RELATED WORKS REVIEW ...............................................12 3.1 MACHINE LEARNING IN DATA NETWORKS – OVERVIEW...........................................................12 3.1.1 Machine learning in data networks ...................................................................................13 3.1.2 Deep learning in data networks.........................................................................................16 3.1.3 Generative models in data networks..................................................................................18 3.1.4 Machine learning in IoT networks.....................................................................................19 3.2 SPECIFIC APPLICATION AREAS...................................................................................................21 3.2.1 Intrusion detection ............................................................................................................. 23 3.2.2 Traffic prediction ............................................................................................................... 29 3.2.3 Type of traffic prediction (traffic classification)................................................................32 3.2.4 QoE estimation...................................................................................................................35 3.2.5 Synthetic data generation ..................................................................................................37 RESEARCH SCOPE ..................................................................................................................... 40 4.1 COMBINATION OF CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS ............................43 4.2 GAUSSIAN PROCESSES...............................................................................................................47 4.3 CONDITIONAL VARIATIONAL AUTOENCODERS FOR CLASSIFICATION .......................................49 4.4 CONDITIONAL VARIATIONAL AUTOENCODERS FOR PRACTICAL DATA SYNTHESIS ...................52 4.5 MACHINE LEARNING FOR TIME-SERIES PREDICTION ................................................................. 54 CONTRIBUTIONS AND LESSONS LEARNED.......................................................................56 5.1 CONTRIBUTIONS ........................................................................................................................ 56 5.2 LESSONS LEARNED ....................................................................................................................59 METHODOLOGY ......................................................................................................................... 61 PAPERS SUMMARY ....................................................................................................................67 7.1 PAPER 1: REVIEW OF METHODS TO PREDICT CONNECTIVITY OF IOT WIRELESS DEVICES.........67 7.1.1 Objectives........................................................................................................................... 67 7.1.2 Datasets ............................................................................................................................. 67 7.1.3 Models................................................................................................................................67 7.1.4 Results/Conclusions ........................................................................................................... 68 7.2 PAPER 2: NETWORK TRAFFIC CLASSIFIER WITH CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS FOR INTERNET OF THINGS.................................................................................................69 7.2.1 Objectives........................................................................................................................... 69 7.2.2 Datasets ............................................................................................................................. 69 7.2.3 Models................................................................................................................................69 4. 5. 6. 7. Doctoral Thesis: Novel applications of Machine Learning to NTAP -2PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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