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A significant part of our daily lives is dependent on the continuous operation of Industrial Automation and Control Systems (IACS). They are used to control the processes of delivering electricity and clean water to our households, to run and supervise manufacturing industries that produce things we use every day. Therefore, undisturbed, safe and secure operation of IACS are highly important for us all. A malfunctioning IACS may cause damage to the environment, stop production of goods or disrupt essential infrastructure.

 The ongoing transformations related to the Industry 4.0 paradigm is having a great impact on IACS, forcing a shift from a rigid, hard-wired system architecture towards a service-oriented structure, where different modules can collaborate dynamically to adapt to volatile production requirements. This shift entails a substantial increase in connectivity and is hence potentially increasing exposure of these systems to cybersecurity threats. Understanding potential risks, and protection against such threats are of great importance.

 Access Control is one of the main security mechanisms in a software system, aiming at limiting access to resources to privileged entities. Within IACS, this mechanism is mainly used as means to limit human users’ privileges on system assets. In the dynamic manufacturing systems of Industry 4.0, there is a need to include fine-grained Access Control also between devices, raising a number of issues with regards to policy formulation and management.

 This licentiate thesis contributes towards the overall goal of improving the security of IACS in the evolving systems of Industry 4.0 by (1) discussing high-level security challenges of large industrial IoT systems, (2) assess one of the main standards for IACS cybersecurity from an Industry 4.0 perspective, (3) derive requirements on Access Control models within a smart manufacturing system, and (4) presenting an algorithm for automatic Access Control policy generation within the context of modular automation, based on formal process descriptions.