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performance has been a combination of CNN [27] and RNN [28]. In this section, we will show all the models considered for this work. The first model analyzed (Fig. 3) was a simple RNN. In particular, we used a variant of an RNN called LSTM [29], which is easier to train (it solves the vanishing gradient problem). An LSTM is trained with a matrix of values with two dimensions: the temporal dimension and a vector of features. LSTM iterates a neural network (cell) with the time sequential feature vectors and two additional vectors associated with its internal hidden and cell states. The final hidden state of the cell corresponds to the output value. Therefore, the output dimension of an LSTM layer is the same as the size of its internal hidden state (LSTM units). In the model of Fig. 3 we add at the end several fully connected layers. Two layers are fully connected when each node of the previous layer is fully forward connected to every node of the consecutive layer. The fully connected layers have been added to all models. Fig. 3. Deep learning RNN model In Fig. 4 a pure CNN network is shown. CNNs were initially applied to image processing, as a biologically inspired model to perform image classification, where feature engineering was done automatically by the network thanks to the action of a kernel (filter) which extracts location invariant patterns from the image. Chaining several CNNs allows extracting complex features automatically. In our case, we have used this image-processing metaphor to apply the technique to a very different dataset. In order to do that, we consider the matrix formed by the time-series of feature vectors as an image. Image pixels are locally correlated; similarly, feature vectors associated with consecutive time slots present a correlated local behavior, which allows us to adopt this analogy. Each CNN layer generates a multidimensional array (tensor) where the dimensions of the image get reduced but, at the same time, a new dimension is generated, having this new dimension a size equal to the number of filters applied to the image. Consecutive CNN layers will further decrease the image dimensions and increase the new generated dimension size. To top off the model it is necessary to transform the tensor to a vector that can be the input to the final fully connected layers. To accomplish this transformation a simple tensor flattening can be done (Fig. 4). Doctoral Thesis: Novel applications of Machine Learning to NTAP - 116PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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