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Simon Barner

Publications and source records attributed to Simon Barner.

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White paper: A perspective on civilian-to-defence research transfer to SDD

Military capability is increasingly determined by software. Yet defence platforms are procured on decade-long timescales, while the software and AI models they carry must evolve in days or hours. This paper calls this mismatch the lifecycle paradox, and argues it is the central problem Software-Defined Defence (SDD) must solve. SDD rests on three dimensions: software and systems engineering (design, procurement, certification), AI engineering (sovereignty and trust of learned components), and connectivity and infrastructure engineering (timely exchange of information among sensors, AI, and operators). The proposed path to resilient SDD starts from civilian technologies, addressed through a continuous, DevOps-style loop: model-based systems engineering and simulation-based testing front-load design and verification; tactical connectivity and low-power edge execution carry that design into contested operation; continuous compliance, assurance, and variability management run as cross-cutting concerns. This loop is sustainable given capabilities already proven in automotive, manufacturing, space, and energy. The next step is validating them under adversarial or defence-certified conditions, with short-, medium-, and long-term paths to closing gaps. Closing the SDD gap while preserving civic benefits is a distributed responsibility: researchers must redirect methods toward adversarial conditions; industry must expose tooling to operational needs; policymakers must shape regulatory instruments; and defence agencies must validate results with operators. Recommendations span three horizons: a short-term baseline of adversarial testing and connectivity pilots; a medium-term pipeline of incremental certification; and a long-term validation closing the loop under operational conditions.

cs.SE

Implementing a Model-based Engineering Tool as Web Application

This paper reports on a study of transferring a desktop-based model-based engineering tool to a web application. The study has been conducted in the WEBMODEL project where the well-established technology stack around the Eclipse platform and the Eclipse Modeling Framework was lifted into a cloud-based environment. As results, a modeling language independent tooling kernel for web-based modeling tools and a minimal prototypical web-based implementation of the AutoFOCUS 3 model-based engineering tool are presented. Furthermore, the report documents experiences and implementation advises gained during the implementation.

cs.SE

Technical Report: Automating Vehicle SOA Threat Analysis using a Model-Based Methodology

While the adoption of Service-Oriented Architectures (SOA) eases the implementation of features such as autonomous driving and over-the-air updates, it also increases the vehicle's exposure to attacks that may place road-users in harm. To address this problem, standards (ISO 21434/UNECE) expect manufacturers to produce security arguments and evidence by carrying out appropriate threat analysis. As key threat analysis steps, e.g., damage/threat scenario and attack path enumeration, are often carried out manually and not rigorously, security arguments lack precise guarantees, e.g., traceability w.r.t. safety goals, especially under system updates. This article proposes automated methods for threat analysis using a model-based engineering methodology that provides precise guarantees with respect to safety goals. This is accomplished by proposing an intruder model for automotive SOA which together with the system architecture and the loss scenarios identified by safety analysis are used as input for computing assets, impact rating, damage/threat scenarios, and attack paths. To validate the proposed methodology, we developed a faithful model of the autonomous driving functions of the Apollo framework, a widely used open-source autonomous driving stack. The proposed machinery automatically enumerates several attack paths on Apollo, including attack paths not reported in the literature.

cs.LO