Machine Learning Algorithms in Modelling of Networks

Dear Colleagues,

Networks form the backbone of many of the familiar and economically vital systems used in our everyday, modern lives, such as electrical grid, computer, telecommunications, and transportation systems. They can effectively model the interconnections and interdependencies between entities in complex systems, whether they are artefacts, such as physical components, or human beings in social networks. Therefore, network-based modeling research can help us to understand how networks behave.

Machine learning is an established research area of artificial intelligence, and its theories and methods have been widely used to solve complex problems in engineering applications and science on the basis of large datasets. At present, network measurements make it possible to obtain large datasets describing the behavior of users and network devices, and provide the data-rich background suitable for a profitable application of machine learning techniques.

This Special Issue is devoted to applications of machine learning in network modeling, including the following fields of applications, without being limited to:

• Communications networks;
• Computer networks;
• Internet of things;
• Social networks;
• Intrusion-detection systems;
• Traffic-anomaly detection;
• Network fault diagnosis;
• Traffic engineering and routing;
• Cyber attacks mitigation;
• Traffic classification;
• Traffic and behavior forecasting.

Prof. Dr. Maurizio Naldi
Dr. Marta Flamini
Guest Editors

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