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which is characterized by including neural networks with multiple layers and a variety of architectures and connectivity between layers. The algorithms related to deep learning have been widely applied in the areas of: image and video processing, audio, word processing, comprehension and translation of natural language, finance, medicine, sales, etc... One of the main objectives of this thesis is to show how its application can be extended to the problems associated with NTAP. Considering the five specific areas of NTAP that are the subject of this thesis, four correspond to classification problems and one with the problem of generating synthetic data that can be used to improve a classification problem. The four areas related with classification pose many challenges to a classification and detection algorithm: 1) highly unbalanced data with labels strongly biased to some of the classes, 2) noisy data and 3) high cardinality of the labels to classify. For these reasons, different types of deep learning algorithms have been explored: generative algorithms (variational autoencoders) and prediction algorithms based on convolutional and recurrent neural networks, considering that these algorithms have shown remarkable results in other business areas. In this thesis we show that these algorithms are applicable to NTAP and the work in this thesis contributes to provide novel architectures based on them. Specifically, it is important to mention the contributions made in this thesis to the areas of: 1) estimation (both detection and prediction) of the quality of a user's experience when viewing multimedia streaming and 2) network traffic classification based on new architectures formed by convolutional neural networks (CNN) and recurrent neural networks (RNN). To generate synthetic data there are many over-sampling algorithms that generate the synthetic data corresponding to a specific class based on the (topological) proximity to existing data of that class. These algorithms need a predefined (often complex) distance definition. In this thesis, an alternative method for the creation of synthetic data is proposed, which is based on a latent probability distribution that is learned from the data and that does not need to assume a predefined distance function. The proposed method consists of a generative model based on a variational autoencoder, with an architecture adapted to the generation of synthetic data associated with specific events. The new method offers operational advantages (speed, simplicity) and quality in the synthesized data (similar probability distributions and improvements in their properties) compared to the usual algorithms. In addition to the novel architectures based on deep learning algorithms, we propose also advances in the application of machine learning models to the problem of network traffic prediction. In this case, due to the time series nature of network traffic, the techniques usually applied have been methods related to the solution of time series prediction problems (e.g. ARIMA, ARIMAX ...). This thesis presents the suitability of alternative machine learning techniques for time series prediction applied to network traffic, as well as a detailed comparison of both options: a) the methods based on classical time series techniques and b) of those based on machine learning. In this case the critical point is the necessary transformation of the training dataset from a time series structure (longitudinal-like data) to a supervised learning structure (matrix-like data). Doctoral Thesis: Novel applications of Machine Learning to NTAP -7PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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