Ph.D / This thesis looks at current security problems within open system environments, that is security problems within heterogeneous computer system environments that are interconnected via computer networks. Thereafter two security models, Kerberos and the Path Context Model, are considered together with their respective ability to address these security problems. Using concepts of the Path Context Model, a new security model, called MOSS (Model for Open System Security), is developed and it is shown how MOSS can address all the security problems identified. Two possible implementations of MOSS are then considered: the one is based on the concept of Static Security Agents (SSAs) for contemporary open system environments, and the other is based on the concept of Roaming Security Agents (RSAs) for object orientated open system environments. The research is concluded with a summary of possible future research considerations

Today, cyber-physical systems (CPSs) are ubiquitous. Whether it is robotics, electric vehicles, the smart home, autonomous driving, or smart prosthetics, CPSs shape our day-to-day lives. Yet, designing and programming CPSs becomes evermore challenging as the overall complexity of systems increases. CPSs need to interface (potentially distributed) computation with concurrent processes in the physical world while fulfilling strict safety requirements. Modern and popular frameworks for designing CPS applications, such as ROS and AUTOSAR, address the complexity challenges by emphasizing scalability and reactivity. This, however, comes at the cost of compromising determinism and the time predictability of applications, which ultimately compromises safety. This thesis argues that this compromise is not a necessity and demonstrates that scalability can be achieved while ensuring a predictable execution. At the core of this thesis is the novel reactor model of computation (MoC) that promises to provide timed semantics, reactivity, scalability, and determinism. A comprehensive study of related models indicates that there is indeed no other MoC that provides similar properties. The main contribution of this thesis is the introduction of a complete set of tools that make the reactor model accessible for CPS design and a demonstration of their ability to facilitate the development of scalable deterministic software. After introducing the reactor model, we discuss its key principles and utility through an adaptation of reactors in the DEAR framework. This framework integrates reactors with a popular runtime for adaptive automotive applications developed by AUTOSAR. An existing AUTOSAR demonstrator application serves as a case study that exposes the problem of nondeterminism in modern CPS frameworks. We show that the reactor model and its implementation in the DEAR framework are applicable for achieving determinism in industrial use cases. Building on the reactor model, we introduce the polyglot coordination language Lingua Franca (LF), which enables the definition of reactor programs independent of a concrete target programming language. Based on the DEAR framework, we develop a full-fledged C++ reactor runtime and a code generation backend for LF. Various use cases studied throughout the thesis illustrate the general applicability of reactors and LF to CPS design, and a comprehensive performance evaluation using an optimized version of the C++ reactor runtime demonstrates the scalability of LF programs. We also discuss some limitations of the current scheduling mechanisms and show how they can be overcome by partitioning programs. Finally, we consider design space exploration (DSE) techniques to further improve the scalability of LF programs and manage hardware complexity by automating the process of allocating hardware resources to specific components in the program. This thesis contributes the Mocasin framework, which resembles a modular platform for prototyping and researching DSE flows. While a concrete integration with LF remains for future work, Mocasin provides a foundation for exploring DSE in Lingua Franca.