Novel applications of Machine Learning to Network Traffic Analysis

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Fig. 4. Deep learning CNN model The previous models can be combined in a single model as presented in Fig. 5. In this combined model, the final tensor of several chained CNNs is reshaped into a matrix that can act as the input to an RNN (LSTM network). To reshape the tensor as a matrix we keep the dimension associated with the filter’s action unchanged, performing a flattening on the other two dimensions, to finally reach a matrix shape. The values produced by the filters of the last CNN will be the equivalent of feature vectors, and the flattened vector produced by the reshaping operation will act as the time dimension needed by the LSTM layer. Fig. 5. Combination of CNN and single-layer RNN Finally, the model introduced in Fig. 6 is similar to the previous model with the inclusion of an additional LSTM layer. When several LSTM layers are concatenated, the LSTM behavior is Doctoral Thesis: Novel applications of Machine Learning to NTAP - 117

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