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3.1.4 Machine learning in IoT networks Even when the machine learning architectures and techniques presented in this thesis are applicable to generic traffic for any data network, the experiments to prove their effectiveness has been mainly done with traffic associated with IoT networks. IoT traffic poses a challenge to current network management and monitoring systems, due to the large number and heterogeneity of the connected devices. The difficulties created by the network traffic in IoT networks have been the reason to choose these networks because the solutions provided are more demanding. The Internet of Things (IoT) has been defined in Recommendation ITU-T Y.2060 (06/2012) as a global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies. It is a network of physical devices embedded in all kind of equipments that autonomously transfer information and operational commands between them or with some centralized system. A complete review of machine learning algorithms applied to IoT problems is presented in [7]. Intrusion detection [34], traffic prediction [35][36][37], characterization and classification of traffic [38], and estimation of video QoE [39][40][41][42], are critical issues in IoT networks. Below is a brief analysis of the importance of NTAP for IoT in the specific areas considered in this thesis: - Considering traffic prediction, from the point of view of an IoT Service Operator (namely a telecommunications operator) it is extremely useful to know in advance the probability distribution of wireless devices connectivity. It is important to anticipate the likelihood that a wireless device will send information over a certain time period, in order to: 1) anticipate the business impact, 2) accommodate the maintenance activity periods to reduce the impact of possible connectivity interruptions, 3) prepare the infrastructure required to reduce the risk of interruption of highly important services and associated devices. In the first work of this thesis [1] is provided a detailed study of the different prediction models available for time-series data and how to modify the training dataset to apply classical machine learning models (random forest, logistic regression, etc...) for time-series prediction. In this work we show that, with the proposed data pre-processing, we can obtain excellent results with a logistic regression or random forest models, which are almost as good as the results obtained with the best time-series model (ARIMAX), but, with an important reduction on the required processing time. - In relation to intrusion detection, a Network Intrusion Detection System (NIDS) is a system which detects intrusive, malicious activities in a host or host’s network. The importance of NIDS is growing as the heterogeneity, volume and value of network data continue to increase. This is especially important for current Internet of Things (IoT) networks, which carry mission-critical data for business services. Intrusion detection must deal with highly noisy and unbalanced datasets for which a classifier based on generative models may be more appropriate. The conditional VAE presented in [3], as part of this thesis work, provides a solution based on generative models that shows better classification results that those obtained with classic solutions: Random Forest, SVM, Logistic Regression and MLP. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 19PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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