Security and Trust in Next Generation Cyber-Physical Systems

Dear Colleagues,

The pervasiveness of cyber-physical systems (CPSs) in the control and supervision of a large number of safety/business-critical applications requires security levels capable of dealing with threats whose frequency and effectiveness are increasing. Due to the computational, communication, and power-related limitations of the devices on which CPSs rely, traditional design and verification methodologies and techniques used in ICT must be improved and adapted to this new context. Examples of CPS include smart grid, autonomous transportation systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics.

As commercial-off-the-shelf devices are now part of the control and supervision of such systems, often supporting legacy protocols not originally designed for an open and un-secure network, designing and ensuring security and trust in such systems is of paramount importance. Cybersecurity threats exploit the increased complexity and connectivity of critical infrastructure systems, placing the nation’s security, economy, public safety, and health at risk.

This Special Issue is devoted to collecting novel and original approaches that ensure the security of current and emerging cyber-physical systems by taking into consideration the unique challenges present in this environment. This Special Issue also aims to foster a research community that is strongly committed to technology transfer. Industrial experiences, showing the application of cutting-edge techniques to real-world case studies, are also welcome.

Topics include but are not limited to the following:

• Formal methods for security and trust;
• Security-by-design approaches;
• Vulnerability evaluation of CPSs;
• Reasoning and machine learning for fraud detection;
• Distributed ledger technology for reputation and trust management in CPSs;
• Cyber resilience foundations and emerging approaches in CPSs;
• Gamification in cyber security;
• Post-quantum security;
• Deep and reinforcement learning for adversarial approaches;
• Artificial intelligence, blockchain and federated learning technologies for data and model security.

Dr. Emanuele Bellini
Dr. Fiammetta Marulli
Dr. Stefano Marrone
Guest Editors

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