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a single pass of the complete training dataset through the training process. All the activation functions were Rectified Linear Units (ReLU) with the exception of the last layer with Softmax activation. The loss function was Softmax Cross Entropy and the optimization was done with batch Stochastic Gradient Descent (SGD) with Adam. In Table I, an added suffix ‘a’ to a model name, implies that the model has only changed the dropout percentage at the dropout layers. Table I. Details of deep learning network models applied to NTC problem Fig. 7. Classification performance metrics (aggregated) vs. network models The best model attains an accuracy of 0.9632, an F1 score of 0.9574, a precision of 0.9543 and a recall of 0.9632 (model CNN+RNN-2a). Analyzing the results, we can see that a simple model, of two CNN layers followed by one Doctoral Thesis: Novel applications of Machine Learning to NTAP - 120PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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