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10. FUTURE LINES OF RESEARCH Considering the lessons learned from this work and the possibilities anticipated by the new models that are actively being developed in the scientific community, we consider the following lines of research could be feasible and provide promising results: • Explore new models of deep learning: Generative Adversarial Networks (GAN) [135], ladder VAE [138], structured VAE [139] and Siamese networks [140]. There is a continuous flow of new models in the very active area of ML research, and there will be great opportunities in applying the new models to different functional areas in networking, as evidenced by a recent survey on deep learning for mobile and wireless networking [29] which shows the large number of works on this area and the future opportunities. In this line, other studies [141][142] clearly indicate the growing importance of the application of machine learning in general and, specifically, deep learning models in other fields of prediction in data networks. • Explore the end-to-end training of a deep learning network with a Gaussian process for regression problems [143]. Gaussian processes are especially suitable for regression problems and the possibility of having a model that combines a neural network architecture with a Gaussian process as the final layer of the network is especially interesting, mainly when the entire model can be trained end-to-end with an appropriate loss function related to the optimization of the Gaussian process [143]. • Explore models that require very little data for training: one-shot learning, zero-shot learning [144][145]. These new models will surely have an important role in networking problems where data is not always available, at least of the type and nature required e.g. in intrusion detection there is a large amount of normal data, but very few samples that correspond to known intrusions. • Explore the application of reinforcement learning models to problems of traffic control and cybersecurity in data networks [146][147][148]. A reinforcement learning algorithm can learn by receiving sparse indications (rewards) of the good or bad actions taken so far. These algorithms are the focus of an important research interest and will surely be important in future ML applications for networking [29], particularly in network and resources management, routing and control problems, and intrusion detection and cybersecurity applications. An interesting extension of the current research would be to apply deep reinforcement learning algorithms to intrusion detection. • Explore the use of aggregated data from different information sources of heterogeneous nature which relates to the study and analysis of security logs. Security logs are critical for all security management aspects. Security logs are the main entry point of information for security threats, having their own ecosystem of functions [58]: acquisition, filtering, normalization, collection management, storage, analysis and long- term storage. Sometimes, the logs ecosystem is highly optimized and automated, but in Doctoral Thesis: Novel applications of Machine Learning to NTAP - 83PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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