Context: Software systems often provide critical functionality or process personal data, requiring compliance with applicable legal regulations. Ensuring legal conformity demands close collaboration between legal and technical experts, but differences in terminology and methodology make this challenging. Objective: In this article, we aim to address the challenges in legal interdisciplinary collaboration by proposing a model-based workflow for ongoing and collaborative legal assessments within the context of threat modeling. Method: The central aspects of the workflow are based on model-driven engineering techniques and were developed through active collaboration between researchers in software engineering and legal informatics/data protection at the KASTEL Security Research Labs. The goal of the collaboration was to integrate the methodologies of both domains into the workflow equally. Result: The proposed workflow centers on maintaining consistency between a legal viewpoint and data flow diagrams, addressing legal subsumption, allowing each discipline to work from its own perspective while providing automated support in threat identification through an extended existing data flow analysis framework that considers legal interpretation. We evaluate the workflow and its modeling artifacts by applying it in the domain of the GDPR, discussing feasibility and applicability, and measuring the accuracy and scalability of the extended data flow analysis. Conclusion: By combining discipline-specific viewpoints with automated consistency and threat identification, the workflow supports collaboration and enables iterative assessments. Our findings suggest that the presented workflow is suitable and operationalizable, but identify potential challenges in practical application or transfer to other legal domains.

Artificial intelligence can revolutionize scientific projects, but scientists face challenges in reusing, integrating, and deploying cost-effective and high-quality machine learning solutions. Determining suitable algorithms and parameters is difficult, especially for non-programmer scientists. Some algorithms, like deep learning-based methods, offer flexibility but require extensive training on annotated data. This poses a hurdle in labor-intensive tasks like biological image segmentation that relies on expert annotations. In this paper, we present a data-driven framework designed to assist scientists in selecting, reusing, and training machine learning solutions for microscopy image segmentation. The framework is based on establishing a mapping between object morphology features and the optimal segmentation algorithms and settings for individual objects. This mapping is iteratively refined through a combination of unsupervised learning and active learning iterations. To expedite convergence, objects are initially clustered based on their morphology. In each active learning iteration, the most informative and uncertain samples are selected and queried within a specific cluster. Through a biological case study, we demonstrate that our method enables the selection and training of segmentation algorithms specific to object types. Additionally, the selective requests for user input significantly reduce the number of user interactions required for this task.

Since cyber-physical systems are inherently vulnerable to information leaks, software architects need to reason about security policies to define desired and undesired information flow through a system. The microservice architectural style requires the architects to refine a macro-level security policy into micro-level policies for individual microservices. However, when policies are refined in an ill-formed way, information leaks can emerge on composition of microservices. Related approaches to prevent such leaks do not take into account characteristics of cyber-physical systems like real-time behavior or message passing communication. In this paper, we enable the refinement and verification of information-flow security policies for cyber-physical microservice architectures. We provide architects with a set of well-formedness rules for refining a macro-level policy in a way that enforces its security restrictions. Based on the resulting micro-level policies, we present a verification technique to check if the real-time message passing of microservices is secure. In combination, our contributions prevent information leaks from emerging on composition. We evaluate the accuracy of our approach using an extension of the CoCoME case study.

Engineering cyber-physical systems secure by design requires engineers to consider security from the ground up. However, current systems engineering processes are not tailored to cyber-physical systems, or lack an integration with security engineering. In this paper, we integrate secure software engineering practices into an engineering process for cyber-physical systems. Thereby, we enable engineers to specify security requirements at the level of systems engineering, and to take effective countermeasures during both platform-independent and platform-specific software engineering. Our key contribution is the integration of threat models for tracing security requirements to countermeasures. We illustrate our approach by an autonomous car with high security requirements.

Cyber-physical systems, e.g., autonomous cars or trains, interact with their physical environment. As a consequence, they commonly have to coordinate with other systems via complex message communication while realizing safety-critical and real-time tasks. As a result, those systems should be correct by construction. Software architects can achieve this by using the MechatronicUML process and language. This paper presents the MechatronicUML Tool Suite that offers unique features to support the MechatronicUML modeling and analyses tasks.