Novel applications of Machine Learning to Network Traffic Analysis

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

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7.2.4 Results/Conclusions ........................................................................................................... 70 7.3 PAPER 3: CONDITIONAL VARIATIONAL AUTOENCODER FOR PREDICTION AND FEATURE RECOVERY APPLIED TO INTRUSION DETECTION IN IOT ......................................................................71 7.3.1 Objectives........................................................................................................................... 71 7.3.2 Datasets ............................................................................................................................. 71 7.3.3 Models................................................................................................................................72 7.3.4 Results/Conclusions ........................................................................................................... 72 7.4 PAPER 4: DEEP LEARNING MODEL FOR MULTIMEDIA QUALITY OF EXPERIENCE PREDICTION BASED ON NETWORK FLOW PACKETS...................................................................................................73 7.4.1 Objectives........................................................................................................................... 73 7.4.2 Datasets ............................................................................................................................. 73 7.4.3 Models................................................................................................................................74 7.4.4 Results/Conclusions ........................................................................................................... 74 7.5 PAPER 5: VARIATIONAL DATA GENERATIVE MODEL FOR INTRUSION DETECTION ....................76 7.5.1 Objectives........................................................................................................................... 76 7.5.2 Datasets ............................................................................................................................. 77 7.5.3 Models................................................................................................................................77 7.5.4 Results/Conclusions ........................................................................................................... 78 TOOLS ............................................................................................................................................ 79 GENERAL CONCLUSIONS AND SUMMARY OF CONTRIBUTIONS...............................80 FUTURE LINES OF RESEARCH ...........................................................................................83 RESEARCH DISSEMINATION PLAN...................................................................................85 LIST OF REFERENCES...........................................................................................................86 PAPERS.......................................................................................................................................95 8. 9. 10. 11. 12. III. PAPER 1.................................................................................................................................................95 PAPER 2...............................................................................................................................................111 PAPER 3...............................................................................................................................................128 PAPER 4...............................................................................................................................................147 PAPER 5...............................................................................................................................................161 POSTER MLSS-2018..........................................................................................................................182 Doctoral Thesis: Novel applications of Machine Learning to NTAP -3

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