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3. RESEARCH CONTEXT AND RELATED WORKS REVIEW The following sections present the research context for this thesis, in two aspects: (1) a general overview of the application of machine learning to data networks and (2) a more specific review of the application of machine learning to the areas of prediction and data generation covered by this thesis. 3.1 Machine learning in data networks – Overview Machine learning [6][7][8] has been extensively applied to data networking problems. This application was done in many cases under different names inside well-known scientific disciplines as signal processing, information theory, coding theory, etc... examples of this can be observed in the application of linear and non-linear regression, statistical models, compression methods, etc...This applications have been usually limited to classic machine learning techniques, however, many recent advances in machine learning are not applied as fully in networking as in other areas (e.g. image, speech and video processing, natural language processing,...). This can be seen in the most updated reviews on the application of machine learning to networking [6][7][8][9], where the reduced application of the latest models in machine learning is pointed out (e.g. deep learning models, generative models), despite being considered important algorithms with a critical contribution in future research. For example, quoting [6] on the importance of machine learning in the future of data networks: “...... The latest breakthroughs, including deep learning (DL), transfer learning and generative adversarial networks (GAN), also provide potential research and application directions in an unimaginable fashion. Dealing with complex problems is one of the most important advantages of machine learning. For some tasks requiring classification, regression and decision making, machine learning may perform close to or even better than human beings. Some examples are facial recognition and game artificial intelligence. Since the network field often sees complex problems that demand efficient solutions, it is promising to bring machine learning algorithms into the network domain to leverage the powerful ML abilities for higher network performance. The incorporation of machine learning into network design and management also provides the possibility of generating new network applications. Actually, ML techniques have been used in the network field for a long time. However, existing studies are limited to the use of traditional ML attributes, such as prediction and classification. The recent development of infrastructures (e.g., computational devices like GPU and TPU, ML libraries like Tensorflow and Scikit- Learn) and distributed data processing frameworks (e.g., Hadoop and Spark) provides a good opportunity to unleash the magic power of machine learning for pursuing the new potential in network systems. .........” Considering these opportunities and envisaged future contributions [10], the objective of this thesis is to provide advances in the application of the latest machine learning models to networking. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 12PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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