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probability distribution according to specific types of intrusions. This is, as far as we know, the first application of a CVAE to the intrusion detection field. Paper 4 [4] presents a new solution based in deep learning networks (a combination of CNN and RNN) to perform QoE detection and short-term prediction of seven QoE anomalies/errors. This is, as far as we know, the first application of a CNN+RNN model to video QoE estimation. In order to perform the estimates, the model uses a small time-series of vectors formed by aggregated information extracted from network packets. Paper 5 [5] proposes the application of a CVAE to generate synthetic data in networking. The architecture of the model is specifically tuned to generate synthetic data (network traffic data) that can be used by an intrusion detection classifier to improve its training performance. Fig. 2. Relationship between research objectives and papers Doctoral Thesis: Novel applications of Machine Learning to NTAP - 11PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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