Carlos Natalino Da Silva
Carlos Natalino is a postdoc with the Optical Networks Unit, focusing on the application of machine learning to telecommunication infrastructure problems. Among the main topics are the design and management of optical networks and cloud computing infrastructures, with special focus on resource and energy efficiency, security, reliability and survivability. He has been involved in several national and international projects funded by research bodies in EU and Brazil. He has also been involved in teaching computer programming courses in Brazil and Sweden. He is an IEEE member.
Showing 16 publications
How to Survive Targeted Fiber Cuts: A Game Theoretic Approach for Resilient SDON Control Plane Design
Machine Learning for Optical Network Security Monitoring: A Practical Perspective
Forecasting power load curves from spatial and temporal mobile data
Network Slicing Automation: Challenges and Benefits
Content placement in 5G‐enabled edge/core datacenter networks resilient to link cut attacks
Machine Learning for Optical Network Security Management
Network-wide localization of optical-layer attacks
Machine Learning Methods for Slice Admission in 5G Networks
One-Shot Learning for Modulation Format Identification in Evolving Optical Networks
Demonstration of Machine-Learning-Assisted Security Monitoring in Optical Networks
Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learning
Enhancing optical network security with machine learning
Reinforcement Learning for Slicing in a 5G Flexible RAN
Experimental Study of Machine-Learning-Based Detection and Identification of Physical-Layer Attacks in Optical Networks
Minimum Cost Design of Cellular Networks in Rural Areas with UAVs, Optical Rings, Solar Panels and Batteries
Cost Benefits of Centralizing Service Processing in 5G Network Infrastructures
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