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3.2 Specific application areas It is interesting to mention possible applications of prediction and detection in the field of data networking. Here we present a summary: - Customer churn prediction - Customer experience, Quality of Experience (QoE) *** - Recommender systems - Congestion prediction and mitigation ** - Network traffic prediction *** - Performance and failure prediction at device, network and service levels - Improve customer care, marketing and pricing - Fraud mitigation - Improve network operations ** - Type of traffic identification *** - Intrusion detection *** - Social media analysis - Customer behaviour - Predictive maintenance * - Support for new networking architectures (e.g. SDN, edge-computing...) ** In the previous list, near each application, there is a series of asterisks that represent the amount of coverage of this application in the research presented in this thesis. Three asterisks mean that we completely cover the corresponding application, two asterisks that we partially cover it, one asterisk a small coverage and no asterisk means there is no coverage. Type of traffic identification and type of traffic prediction, which are two of the application areas fully covered by this thesis, are identified in a recent IEEE Network Survey [6] as part of the most recent breakthroughs in the application of deep learning and other machine learning techniques to data networks. QoE estimation is also fully covered by this thesis. Finally, the thesis broadly covers intrusion detection from different angles: a) to improve detection and b) to generate synthetic data that can be used to improve detection. One of the more desirable functions for any data network is the ability to perform accurate and robust detection/prediction of network characteristics which have an impact in the network operations and management, such as: (1) the future traffic and/or activity in the network (2) the type-of-service used by a network flow (3) the presence of security intrusions or malicious activities in the network (4) the quality of experience (QoE) of a customer using content transmitted by the network (multimedia content). The first point is important because a data network must handle many diverse service requirements coming from their associated devices; therefore, any knowledge about future traffic behaviour is important to anticipate best resources allocation and possible network reconfigurations [35][36][37][43][44]. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 21PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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