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The second point is crucial in order to implement effective management of network policy and resources, since the network must react differently according to the service profile for each network connection [45][46][47]. The detection of the service that is being used by a network flow is known in the literature as Network Traffic Classification (NTC). It is clear the importance of the third point for any network operation, since the ability to detect intrusive, malicious activities or policy violations in a data network is critical due to the complex, sensitive and ever-increasing economic importance of modern network services [34][48]. The fourth point is relevant as the demand for video services increases in parallel with the storage and processing capabilities of these services by the network itself (edge computing, cloud services...). It is now possible to host highly demanding video processing services in the network, which allows to offer new network capacities based on automatic and intelligent analysis of video transmissions and QoE-aware network management and video traffic prioritization and scheduling [39][40][41][42]. Hence the importance of more robust and accurate QoE predictors that can make better use of the new available platforms (e.g. GPUs). In in this thesis is proposed a new QoE predictor which is based in a deep learning model that is especially suitable for these new platforms (e.g. GPU) and that provides better classification results that more classic state-of-the-art-art machine learning algorithms. Prediction and detection in the four areas considered (traffic estimation, classification of type of traffic, intrusion detection and QoE estimation) present many challenges to a classification algorithm: scarce data, highly unbalanced datasets with a few labels having most samples, noisy data, complex and numerous features, highly correlated features and multi-class classification with usually many values. This is the reason to explore new algorithms, such as: (a) generative algorithms (variational autoencoders) that can handle noisy and complex features within a stochastic framework, and (b) classification algorithms based on convolutional and recurrent neural networks. In the latter case, our hope was that their excellent representational learning capabilities could be extended to these new areas, taking into account their good results in image, voice and text processing, thus avoiding the need for complex feature-engineering that would otherwise be necessary. The datasets and computing resources available were additional aspects considered when selecting the application areas covered by this research work. The use of machine learning techniques in other areas of application generally requires a large infrastructure due to the volume and complexity of the data (e.g., social network analysis, customer behaviour ...). Another reason for selecting these areas was that they are more technical and less involved with the client or economic aspects of the services, which are generally more problematic due to confidentiality and commercial issues. An exception to this last point is the work done to estimate QoE that is directly related to the user experience. In this case, we created an experimental setup with real individuals who evaluated several video transmissions under different network conditions. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 22PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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