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9. GENERAL CONCLUSIONS AND SUMMARY OF CONTRIBUTIONS The application of machine learning techniques to data networking and telecommunications is providing fruitful results; however, there are still many application opportunities, which will arise in the future as soon as the new methods developed, mainly in the very active space of deep learning research, move beyond their initial research scope: image processing, natural language processing, automatic translation, voice recognition... to the field of networking and telecommunication. This thesis tries to provide a contribution in that direction to shorten the gap between the advances of research in machine learning and its application to the Telco area. In the following paragraphs, a summary of the contributions of the different papers is provided: Paper1: “Review of methods to predict connectivity of IoT wireless devices” • Contribution_1: Study of the activity behaviour of IoT devices in a real environment. • Contribution_2: Thorough comparison of application of "time-series" and "cross- sectional" models to IoT future activity prediction. • Contribution_3: The lessons learned, and results obtained for the best algorithms can be applicable to a real environment with a big number of IoT devices. The results present a very high forecasting accuracy. • Contribution_4: As far as we know, it is the first reported application of a Random Forest algorithm to a time-series prediction scenario. • Contribution_5: It is shown that one week of historical data is enough to provide good forecasts and the method with best absolute accuracy performance is ARIMAX, but Logistic Regression or Random Forest could be better operational models due to the excessive training time of ARIMAX. Paper 2: “Network Traffic Classifier with Convolutional and Recurrent Neural Networks for Internet of Things” • Contribution_1: First application, as far as we know, of a CNN+RNN model to the traffic classification problem (NTC). • Contribution_2: The proposed method provides better detection results than alternative algorithms without requiring any feature engineering, which is usual when applying other models. • Contribution_3: The resulting model is applicable to a very unbalanced dataset and using only a few packages per flow as predictors. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 80PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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