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Paper 3: “Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT” • Contribution_1: First application, as far as we know, of a C-VAE model to an intrusion detection problem. • Contribution_2: It proposes a new approach for attacks classification and synthetic features generation based in a generative model. • Contribution_3: It obtains better prediction results than classic machine learning models. • Contribution_4: It provides the capacity to generate synthetic features associated to particular intrusion events. Paper 4: “Deep learning model for multimedia Quality of Experience prediction based on network flow packets” • Contribution_1: First application, as far as we know, of a CNN+RNN model to video QoE prediction. • Contribution_2: Prediction based on network packet information. • Contribution_3: Network flows treated as pseudo-images that allow applying a CNN. • Contribution_4: Excellent prediction performance for not extremely unbalanced labels with a small dataset. Paper 5: “Variational data generative model for intrusion detection” • Contribution_1: First application, as far as we know, of a VAE as a generative model for intrusion detection • Contribution_2: To provide means to demonstrate that the data generated is similar to the original data, and, at the same time, have enough variability to be effective in improving the detection performance of several classifiers when used together with the original data. • Contribution_3: To provide the ability to synthesize the new samples from the intrusion labels to which the synthetic data should belong, with the advantage of not relying on specific samples associated with the labels. • Contribution_4: To propose a new over-sampling algorithm whose synthetic data improves the classification results of classic SOTA over-sampling techniques. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 81PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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