Novel applications of Machine Learning to Network Traffic Analysis

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Novel applications of Machine Learning to Network Traffic Analysis ( novel-applications-machine-learning-network-traffic-analysis )

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4. RESEARCH SCOPE As a summary, in Table 6 is presented the research scope, considering the objectives, areas of application and methods and algorithms applied. Objective Area Method Algorithms Synthesize data Intrusion/security Generative/Unsupervised Conditional VAE Synthesize data Intrusion/security Unsupervised/Supervised SMOTE, ADASYN Detection Intrusion/security Generative/Unsupervised Conditional VAE Detection Intrusion/security Supervised Random Forest, Linear SVM, Logistic Regression, MLP Detection/Prediction Type of traffic Supervised CNN, LSTM classification Prediction Traffic estimation Supervised (based in transformed cross-sectional data) Random Forest, Logistic Regression, Bayesian Logistic Regression, GBM Prediction Traffic estimation Time-series (based in original Exponential Smoothing, time-series data) HMM, ARIMA, ARIMAX Prediction Quality of Experience estimation Table 6. Scope of this research Supervised CNN, LSTM, Gaussian Process In Table 6 are presented all the algorithms that have been used for this work, some of these algorithms have been used to carry on analysis and comparative assessments between different models and others are new proposals/architectures/models which are part of the contributions of the thesis, It is interesting to have a comparative summary between the broad categories of methods related with machine learning, deep learning and generative models. This is a broad comparative that position each group by their main differences, even when they are actually interconnected (Fig 4). Doctoral Thesis: Novel applications of Machine Learning to NTAP - 40

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