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For [2], it is the first time, as far as we know, that a CNN+LSTM network is applied to an NTC problem. A CNN network is mainly intended to deal with image data, and the application to NTC has been possible with our initial intuition to assimilate the vector time-series extracted from network packets as an image. The good results obtained confirm that the initial intuition was correct, and therefore CNNs are valid candidates for dealing with vector time-series. Similar reasoning can be applied to [4] which also presents the application of a CNN+LSTM to a video QoE problem. In this case, it was necessary to prepare the dataset to provide information chunks that could be assimilated to images. In this case, it was also an interesting discovery to observe the importance of the CNN network, which is more critical, for prediction performance, than the RNN network, which is curious given the time-series nature of the data also in this case. For this work, we also used a Gaussian process classifier as the final layer of the entire model. The interesting finding in this case was to observe that by adding this last layer the performance improved, but in a non-significant way. In the case of [3], it is also the first time, as far as we know, that a C-VAE is applied to an intrusion detection problem. The inclusion in the network of the intrusion label (in the C-VAE case) is particularly important for intrusion detection since it generates a resulting model which is less complex than other classifier implementations based on a pure VAE. The model operates creating a single model in a single training step, using all training data irrespective of their associated labels, while a classifier based on a VAE needs to create as many models as there are distinct label values, each model requiring a specific training step (one vs. rest). Training steps are highly demanding in computational time and resources; therefore, reducing its number from n (number of labels) to 1 is an important improvement. In addition, the model is also able to perform feature reconstruction, for which there is no previous published work. Both capabilities can be used in current Network Intrusion Detection Systems (NIDS), which are part of network monitoring systems, and particularly in IoT networks [34]. Similarly, [5] provides the application of a C-VAE to generate synthetic data in networking for an intrusion detection problem. To arrive to the best architecture for the proposed model it was necessary to check: a) several network configurations, b) options on prior probability distributions for the latent and final layers and c) to consider alternative loss functions. The work in [1] is an exception in terms of models applied since we have not used any deep learning model. In this case, we have applied a set of well-known classic machine learning models (Random Forest, Logistic Regression...) to a time-series prediction problem that is usually treated with time-series algorithms (ARIMA, Hidden Markov Model-HMM,...). The challenge has been in transforming a time-series dataset to a cross-sectional format suitable for the non-time-series models and selecting the appropriate features in the new formatted dataset. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 65PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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