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An important type of data network is related to IoT (Internet of Things) devices. This type of network imposes some new and difficult requirements due to the large number of associated devices of heterogeneous nature and with very different connectivity and service characteristics. Therefore, IoT networks are a good place to test new machine learning models, to assess whether they can provide an improvement in their performance, manageability and/or security. This is the reason why, even when the results obtained in this thesis are applicable to any type of data networks, the IoT networks have been the focus of many of the experiments carried out in the thesis. The modality of this thesis is the compendium of publications in JCR-indexed journals in the telecommunications and data networking field, with a total of five papers published. This thesis focuses only on the published papers. All the papers use related techniques (machine learning and deep learning), some related problems (NTAP), with common objectives (detection and prediction) and that revolve around a common field of action (data networks). The papers together form a coherent line of work, aimed at the application of advanced techniques of machine learning to the resolution of complex problems of analysis and prediction raised in new data network architectures. The first paper [1] of this compendium: "Review of methods to predict connectivity of IoT wireless devices", focuses on the prediction of activity of wireless devices using different machine learning techniques and classical techniques for time-series prediction, unifying both types of techniques in a common framework and providing a comparative analysis between them and the behaviour of the connected devices. The second paper [2]: "Network traffic classifier with convolutional and recurrent networks for Internet of Things", studies the application of deep learning models based on convolutional and recurrent neural networks to the prediction of the type of service of a network flow, using exclusively information of the headers of the network flow packets. The third paper [3]: "Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT", investigates the use of generative models based on variants of variational autoencoders to the detection of intrusions in data networks, as well as for the synthesis of features associated with different types of intrusions. The fourth paper [4]: “Deep learning model for multimedia Quality of Experience prediction based on network flow packets”, proposes a new classifier to perform QoE estimation of multimedia content transmitted by a data network. It is based on a deep learning model (convolutional and recurrent networks) plus a Gaussian process as the final layer. The resulting classifier can perform QoE detection (current time) and prediction (short-term forecast) using exclusively aggregated information extracted from the network packets. The fifth paper [5]: "Variational data generative model for intrusion detection ", presents the possibility of generating synthetic traffic data (with both discrete and continuous features), where the synthetic data can be conditioned to different types of intrusions (security attacks). In this way, the probability distribution of the features for the synthetic traffic follows the distribution of the real features for each type of intrusion. Moreover, we show that the synthetic traffic can be used as new training data, improving the detection results of well- known classifiers. Doctoral Thesis: Novel applications of Machine Learning to NTAP -8PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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