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Authors in [9] present a complete review of deep learning for networking considering also future directions. Of all applications considered: wireless sensor networks, network traffic classification, network flow prediction, social networks, mobility prediction, cognitive radio, self-organized networks and routing, the last four are the focus of more work in deep learning, mainly using Deep Belief Networks, RNN, CNN and MLP with several layers. Nevertheless, the inclusion of deep learning in networking remains scarce, as mentioned in [9], quoting: “While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention.”. Another recent and comprehensive review of deep learning for mobile and wireless networking is provided in [29]. In addition to a detailed review of current works related to deep learning applied to networking, the section on future research perspectives is especially interesting, as it points out the promising areas of future research: (a) Deep Learning for Spatio-Temporal Data Mining, (b) Deep learning for Geometric Data Mining, (c) Deep Unsupervised Learning and (d) Deep Reinforcement Learning for Network Control. Additionally, the lack and difficulty of accessing data sets related to network activity is mentioned as one of the most serious problems in the application of deep learning to networking. This is due to privacy concerns of operators and users, which are completely reasonable, but nevertheless hamper the development and application of deep learning in this area. In particular, for the application of deep learning to intrusion detection, in [30] is provided an interesting review and taxonomy of deep learning algorithms in this area. They differentiate between the discriminative (e.g. CNN) and generative models (e.g. VAE, Boltzmann Machines, ...), emphasizing the importance of autoencoders (especially stacked autoencoders) as feature extractors, which in many cases serve as the first stage to perform the classification of intrusions. It is also important to appreciate the difficulties that deep learning can have in a strictly regulated area such as networking due to its difficulty to provide an interpretation of results. For example, European Union’s General Data Protection Regulation require such an interpretability of the results when the ML model is used to make decisions without human intervention. This problem has produced an interesting research activity to facilitate the interpretation of the results provided by a deep learning network. This “black box” problem of deep learning models is related to the problem of adversarial inputs (slightly modified inputs that cause an intentional change in the results) and the need to provide credibility to the decisions made by the network. In this line, the work in [31] analyzes this problem and presents a possible solution to estimate the nonconformity (and interpretability) of results, by finding a subset of training samples similar in cosine distance to the results produced by all layers of the network; they apply the k-Nearest Neighbors algorithm to identify the subset of similar inputs which are the basis for facilitating a later interpretation of the results. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 17PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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