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The emergence of Connected, Cooperative, and Automated Mobility (CCAM) systems has significantly transformed the safety assessment landscape. Because they integrate automated vehicle functions beyond those managed by a human driver, new methods are required to evaluate their safety. Approaches that compile evidence from multiple test environments have been proposed for type-approval and similar evaluations, emphasizing scenario coverage within the system’s Operational Design Domain (ODD). However, aligning diverse test environment requirements with distinct testing capabilities remains challenging. This paper presents a method for evaluating the suitability of test case allocation to various test environments by drawing on and extending an existing ODD formalization with key testing attributes. The resulting construct integrates ODD parameters and additional test attributes to capture a given test environment’s relevant capabilities. This approach supports automatic suitability evaluation and is demonstrated through a case study on an automated reversing truck function. The system’s implementation fidelity is tied to ODD parameters, facilitating automated test case allocation based on each environment’s capacity for object-detection sensor assessment.

The deployment of automated functions that can operate without direct human supervision has changed safety evaluation in domains seeking higher levels of automation. Unlike conventional systems that rely on human operators, these functions require new assessment frameworks to demonstrate that they do not introduce unacceptable risks under real-world conditions. To make a convincing safety claim, the developer must present a thorough justification argument, supported by evidence, that a function is free from unreasonable risk when operated in its intended context. The key concept relevant to the presented work is the intended context, often captured by an Operational Design Domain specification (ODD) specification. ODD formalization is challenging due to the need to maintain flexibility in adopting diverse specification formats while preserving consistency and traceability and integrating seamlessly into the development, validation, and assessment. This paper presents a way to formalize an ODD in the Pkl language, addressing central challenges in specifying ODDs while improving usability through specialized configuration language features. The approach is illustrated with an automotive example but can be broadly applied to ensure rigorous assessments of operational contexts.

Using machine learning to detect and identify cyberattacks in Industrial Control Systems (ICS) offers a promising solution for uncovering zero-day attacks that traditional rulebased models cannot detect. However, applying ML-based intrusion detection in ICS environments presents challenges, including limited availability of attack data and difficulty in accurately identifying attack types. This paper addresses these challenges by proposing two key strategies. First, we demonstrate that the predictable traffic patterns of ICS networks enable the use of semi-supervised learning models for attack detection. We validate this approach using a benchmark dataset, showing that semi-supervised models achieve comparable performance to fully supervised models while relying solely on training with normal network data. Second, we propose a sequence-based approach for attack identification, using temporal data to improve the accuracy of identifying specific attack types. Our experiments reveal that incorporating historical network parameters improves the attack identification. Our research underscores the potential of semisupervised learning for effective attack detection and highlights the importance of incorporating network temporal properties to improve attack identification. 

In the labyrinthine world of real-time systems, few names resonate as powerfully as that of Prof. Wang Yi. As the co-inventor of the UPPAAL model checker, alongside Prof. Kim G. Larsen and Prof. Paul Pettersson, Wang has charted new territories in computer science, setting a benchmark that stands as a testament to excellence.

Dynamic access control in industrial systems is becoming a concern of greater importance as a consequence of the increasingly flexible manufacturing systems developed within the Industry 4.0 paradigm. With the shift from control system security design based on implicit trust toward a zero-trust approach, fine grained access control is a fundamental requirement. In this article, we look at an access control enforcement architecture and authorization protocol outlined as part of the Open Process Communication Unified Automation (OPC UA) protocol that can allow sufficiently dynamic and fine-grained access control. We present an implementation, and evaluates a set of important quality metrics related to this implementation, as guidelines and considerations for introduction of this protocol in industrial settings. Two approaches for optimization of the authorization protocol are presented and evaluated, which more than halves the average connection establishment time compared to the initial approach.

This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage.

Assemblies of machinery commonly require control systems whose functionality is based on application software. In Europe, such software requires high safety integrity levels in accordance with the Machinery Directive (MD). However, identifying the essential regulatory requirements for the safety approval is not an easy task. To facilitate this job, this paper presents a process for Safety Argumentation for Machinery Assembly Control Software (SAMACS). We are inspired by patterns provided in the Goal Structuring Notation (GSN) and the use of contracts in safety argumentation. SAMACS contribution is aligning those methods with the MD by adopting EN ISO 13849. In particular, we define safety goals based on expected software contribution to control system safety and the standard guidance. Software safety goals are detailed into software safety requirements and expressed further as contracts, which shall be verified with prescribed techniques. We apply SAMACS to a case study from a European mining company and discuss the findings. This work aims at helping practitioners compose the safety case argumentation necessary to support machinery integration approval in Europe. 

This paper presents a cybersecurity solution designed to fortify Industrial Control Systems (ICS) against cyberattacks. The proposed solution integrates a Network-based Intrusion Detection System (NIDS) with a Decision Support System (DSS), leveraging machine learning to detect anomalies in network data and employing a filtering mechanism to reduce false alarms. The NIDS protects a simulated ICS testbed, detecting anomalies and forwarding them to the DSS for further analysis and selection of mitigation strategies. We outline the system architecture and showcase promising outcomes from a prototype implementation. Our proof of concept evaluation demonstrates high accuracy in detecting attack scenarios. Challenges such as detection delays between attacks and potential mitigations high-light areas for future improvement. This research contributes to bridging the gap between ML-based IDS and security solutions, paving the way for enhanced cybersecurity in ICS environments. 

Industrial control systems are undergoing a trans-formation driven by business requirements as well as technical advances, aiming towards increased connectivity, flexibility and high level of modularity, that implies a need to revise existing cybersecurity measures. Access control, being one of the major security mechanisms in any system, is largely affected by these advances.In this article we investigate access control enforcement architectures, aiming at the principle of least privilege1 in dynamically changing access control scenarios of dynamic manufacturing systems. Several approaches for permission delegation of dynamic access control policy decisions are described. We present an implementation using the most promising combination of architecture and delegation mechanism for which available industrial standards are applicable.

In this paper, we analyze the scalability and performance of a persistent, fault-tolerant storage approach that provides data availability and consistency in a distributed container-based architecture with intended use in industrial control applications. We use simulation to evaluate the performance of this storage system in terms of scalability and failures. As the industrial applications considered have timing constraints, the simulation results show that for certain failure patterns, it is possible to determine whether the storage solution can meet critical deadlines. The presented approach is applicable for evaluating timing constraints also of other container-based critical applications that require persistent storage.