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employing a full range of prediction/classification algorithms (MLP, RNN, decision trees, ensembles...) with a focus on feature selection techniques and anomaly detection based on unsupervised models (K-means, SOM...). QoS/QoE management can use ML for both prediction and adaptation, although all research focuses on prediction using most prediction/classification algorithms (SVM, Random Forest, MLP, K-NN, Naïve Bayes...) without reported works using deep learning. Network security is an intensive area of research where the majority of ML classification algorithms are applied, but in this case, there are more works that employ deep learning algorithms, mainly Deep Belief Networks (DBN), with also many unsupervised learning algorithms used for anomaly detection problems (SOM, one-class SVM...). It is also interesting the three most important points that are mentioned as problematics for a stronger adoption of ML in networking: a) lack of real-world data, b) the need for standard evaluation metrics and c) specific theory and ML techniques for networking (since most of the techniques are developed for other fields). Doctoral Thesis: Novel applications of Machine Learning to NTAP - 15PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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