SS01 - AI-based Safe, Secure, and Sustainable (I)IoT

Special Session Organized by

Muhammad Taimoor Khan, University of Greenwich, UK, and Dimitrios Serpanos, ISI Athena, ECE, University of Patras, Greece, and Howard Shrobe, MIT CSAIL, USA, and Kunio Uchiyama, AI Chip Design Centre, Japan,

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Computing constitutes a fundamental component of the emerging initiatives like Society 5.0, Industry 5.0, Healthcare 5.0, and Agriculture 5.0 (aka X 5.0), which combine cyber and physical spaces (i.e., processes) and require control and monitoring techniques for their operation and management. In X 5.0, people, things, devices, and systems are connected in cyberspace and operate exploiting automated methods, including machine learning (ML) and artificial intelligence (AI). Such operation and management bring new value to industry and society in ways not previously possible. Typical cyber-physical systems (CPS) are based on (I)IoT (Industrial - Internet of Things) and (I)CPS (Industrial - Cyber Physical Systems) and have applications in all critical infrastructure domains with strict real-time requirements, such as healthcare, electric grid, transportation, to name a few. Intentional or accidental errors/failures/attacks to these systems have highly severe consequences. Therefore, novel design methodologies are required to ensure that design of real-time cyber-physical systems and applications in the emerging Society 5.0 are free of vulnerabilities, threats, and attacks. Since the physical part of CPS involves several processes, typically, it is challenging to ensure that the design is free from all known vulnerabilities. It is necessary to develop run-time monitoring and analysis techniques that can help to detect run-time incidents by observing the processes and their data. Furthermore, adequate modeling of CPS physical processes and corresponding cyber and physical attacks is fundamental to systematically model, analyze, and verify real-time security of CPS. Importantly, since AI and machine learning have demonstrated their success in many application areas, including cybersecurity, this special session focuses on investigating AI, machine learning, and formal methods-based techniques to develop safe, secure, privacy, and law-aware real-time cyber-physical systems, digital twins, and smart cities at all levels, from hardware components to applications.

Topics under this session include (but not limited to)

  • Design-time and run-time safety, security, privacy, and law in modern systems, e.g., X 5.0, Digital Twins, ICPS, and IIoT.
  • Data-driven (AI and Machine Learning or model)-based safety, security, privacy, and law in cyber-physical systems (CPS), networks, and communication
  • Prevention, detection, and mitigation techniques for real-time CPS (RT-CPS) applications against cyber, non-cyber, and cyber-non-cyber threats
  • Hardware design for safe, secure, privacy, and law-aware RT-CPS
  • Vulnerability analysis of RT-CPS applications
  • Attack modeling and performance analysis of RT-CPS
  • Formal methods (FM)-based safety and security of critical systems at design-time and run-time
  • Safety, security, and privacy of citizens in X 5.0 including manmade and natural cyber and non-cyber threats, pandemics, and disasters
  • Methodologies and tools for analysis, compliance, and enforcement of law and regulations for safety, security, and/or privacy
  • Methodologies and tools for compliance testing and standardization
  • CAD tools for AI-based cyber-physical systems (CPS)
  • CAD tools for safe, secure, privacy, and law-aware RT-CPS
  • Case studies for AI and machine learning-based RT-CPS
  • Case studies for digital law compliance and regulations in RT-CPS
  • Benchmarks for security, safety, privacy, and/or law in RT-CPS
  • Challenges in modeling, analysis, safety, security, privacy, and law of RT-CPS