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Modern industrial control systems has evolved into industrial cyber-physical systems (ICPS) and Industrial Internet-of-Things (IIOTs), which combines cyber and physical industrial processes together using control and monitoring techniques. Typically, these systems have applications in all critical infrastructure domains with strict real-time requirements, e.g., healthcare, electric grid, transportation, to name a few. Any intentional or accidental error/threat to such systems have very severe consequences. Therefore, novel design methodologies are required to ensure that design of real-time cyber physical system applications (RT-CPS) is free of certain vulnerabilities and attacks. Since, physical process of CPS involves many such systems, thus, it is very challenging to ensure that the design is free from all known vulnerabilities or attacks. Therefore, it is required to develop run-time monitoring and analysis techniques that can help to detect run-time threats by observing the processes and their data. Furthermore, adequate modelling of CPS physical processes and corresponding cyber and physical attacks is fundamental to systematically model, analyse and verify real-time security of CPS. Importantly, since AI and machine learning has demonstrated their success in many application areas including cyber security, this special session is focused on investigating AI, machine learning and formal methods based techniques to develop safe and secure real-time cyber physical systems.