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highly unbalanced distribution of service labels, we provide the following performance metrics for each option: accuracy, precision, recall, and F1. Considering all metrics, F1 can be considered the most important metric in this scenario. F1 is the harmonic mean of precision and recall and provides a better indication of detection performance for unbalanced datasets. F1 gets its best value at 1 and worst at 0. We base our definition of accuracy, F1, precision, and recall in the following four previous definitions: (1) false positive (FP) that happens when there is actually no detection but we conclude there is one; (2) false negative (FN) when we indicate no detection but there is one; (3) true positive (TP) when we indicate a detection and it is real and (4) true negative (TN) when we indicate there is no detection and we are correct. Considering these previous definitions: Accuracy = TP + TN (1) TP+TN+FP+FN Precision = TP (2) TP+FP Recall = TP (3) TP+FN F1 = 2 Precision × Recall (4) Precision + Recall We have used Tensorflow to implement all the models, and the python package scikit-learn to calculate performance metrics. All computations have been performed in a commercial PC (i7-4720-HQ, 16GB RAM). A. Impact of network architecture We have tried different deep learning architecture models to see their suitability for the NTC problem. In order to build the different architectures, we have considered different combinations of RNN and CNN: RNN only, CNN only, and various arrangements of a CNN followed by an RNN. In all cases, we have added at the end two additional fully connected layers. In Table I, we provide a description of the different architectures and in Fig. 7 we present their performance metrics. From Fig. 7, we can see that the model CNN+RNN-2a gives the best results for both accuracy and F1. The architecture description provided in Table I is as follows: Conv(z,x,y,n,m) stands for a convolutional layer with z filters where x and y are the width and height of the 2D filter window, with a stride of n and SAME padding if m is equal to S or VALID padding if m is equal to V (VALID implies no padding and SAME implies padding that preserves output dimensions). MaxPool(x,y,n,m) stands for a Max Pooling layer where x and y are the pool sizes, with a stride of n and SAME padding if m is equal to S or VALID padding if m is equal to V (VALID implies no padding and SAME implies padding that preserves output dimensions). BN stands for a batch normalization layer. FC(x) stands for a fully connected layer with x nodes. LSTM(x) stands for an LSTM layer where x is the dimensionality of the output space; in the case of several LSTM in sequence, each LSTM, except the last one, will return the successive recurrent values which will be the entry values to the following LSTM. DR(x) stands for a dropout layer with a dropout coefficient equal to x. In all cases, the training was done with a number of epochs between 60-90 epochs, with early stopping if the last 10 epochs did not improve the loss function. We consider an epoch as Doctoral Thesis: Novel applications of Machine Learning to NTAP - 119PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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