To encourage the uptake of AI in industrial use cases, tools are required to support the engineering process throughout the life cycle of the AI application that is central to the use cases. Providing guidance on using AI in industrial setups is vital for creating trustworthy, reliable, and ethically compliant AI-based solutions. The related standardization landscape and available guideline repositories are large, scattered over the web, change rapidly, and are hard to keep up with. This limits the access and ease of use of these standards and guidelines. To address these limitations, we propose developing a self-assessment tool, empowered through AI algorithms and models such as Large Language Models, to improve the process of accessing and benefiting from those standards and guidelines. This self-assessment tool will support various user groups while engineering their applications by identifying the most applicable guidelines according to the individual attributes of the specific user group. We argue that modeling specific attributes and mapping appropriate controls for self-assessments could be achieved by applying AI-based technologies. This paper outlines our ongoing efforts concerning the suggested supporting tools, offering a human-centric methodology. Additionally, we present initial results demonstrating how the needs of a particular user group can be accurately modeled. The results of this study will be used for applications that are deploying AI in an industrial setting with the objective of enabling the two most important goals of Industry 5.0, which are the well-being of workers at the center of the production process, and sustainable and resilient industries.

We propose the AQUILA framework (Adaptive Quality Assurance in Industrial Applications), a concept for digitalization in Industry 4.0 to support the entire industrial manufacturing chain, laying the groundwork for adaptive quality assurance in times of disrupted supply chains and, due to the COVID-19 pandemic, restricted travel possibilities. To that end, our proposed framework allows for the definition and description of industrial processes, quality assurance and testing protocols, and training scenarios in a comprehensive notation based on BPMN, and supports users in task execution, documentation, and evaluation by providing smart glass-based HCI with eye tracking technology, delivering a combination of process documentation, context-sensitive AR visualization, gaze-based interaction schemes, and remote maintenance and assistance functionality.

Cyber security is increasingly recognized as an essential topic for automotive systems, especially in the area of connected and automated driving. Upcoming regulation defines requirements for cyber security on multiple levels in the automotive domain in order to achieve type approval. On the organizational level, a cyber security management system (CSMS) which covers the whole vehicle lifecycle and ecosystem is required. In addition, an argumentation for the cyber security of each vehicle type for which type approval is requested has to be given. Due to the novel nature of these requirements compared to existing type approval requirements, a test phase is ongoing. The components and scope of a CSMS are an open issue. We give an overview of the requirements for a CSMS, identify approaches and gaps, and give an outlook towards a potential framework which can address the requirements.

Modern automobiles are considered semi-autonomous vehicles regarding new adaptive technologies. New cars consist of a vast number of electronic units for managing and controlling the functional safety in a vehicle. In the vehicular industry, safety and security are considered two sides for the same coin. Therefore, improving functional safety in the vehicular industry is essential to protect the vehicle from different attack scenarios. This work introduces an ontology-based model for security verification and validation in the vehicular domain. The model performs a series of logical quires and inference rules to ensure that the security requirements are fulfilled. It endeavors to enhance the current security state of a vehicle by selecting additional security requirements that can handle existence security weaknesses and meet the actual security goal.

In the last years the interconnection and ongoing development of physical systems combined with cyber resources has led to increasing automation. Through this progress in technology, autonomous vehicles, especially autonomous trains are getting more attention from industry and are already under test. The use of autonomous trains is known for increasing operation efficiency and reduction of personnel and infrastructure costs, which is mostly considered for main tracks. However, for less-used secondary lines, autonomous trains and their underlying sensor infrastructure are not yet considered. Thus, a system needs to be developed, which is less expensive for installation and operation of these trains and underlying infrastructure for secondary lines. Therefore, this position paper describes the process of how to derive an approach to help develop a digital interlocking system at design time for the use with secondary railway lines. In this work, we motivate the necessary research by investigating gaps in existing work as well as presenting a possible solution for this problem, a meta-model. The model considers safety, security as well as interoperability like 5G and socio-technical aspects to provide a holistic modeling approach for the development of the interlocking system for industrial secondary line use cases.

In the emerging industrial Internet of Things (IIoT) era, machine-to-machine (M2M) communication technology is considered as a key underlying technology for building IIoT environments, where devices (e.g., sensors, actuators, and gateways) are enabled to exchange information with each other in an autonomous way without human intervention. However, most of the existing M2M protocols that can be also used in the IIoT domain provide security mechanisms based on asymmetric cryptography resulting in high computational cost. As a consequence, the resource-constrained IoT devices are not able to support them appropriately and thus, many security issues arise for the IIoT environment. Therefore, lightweight security mechanisms are required for M2M communications in IIoT in order to reach its full potential. As a step toward this direction, in this paper, we propose a lightweight authentication mechanism, based only on hash and XOR operations, for M2M communications in IIoT environment. The proposed mechanism is characterized by low computational cost, communication, and storage overhead, while achieving mutual authentication, session key agreement, device's identity confidentiality, and resistance against the following attacks: replay attack, man-in-the-middle attack, impersonation attack, and modification attack.

Internet technology has changed how people work, live, communicate, learn and entertain. The internet adoption is rising rapidly, thus creating a new industrial revolution named "Industry 4.0". Industry 4.0 is the use of automation and data transfer in manufacturing technologies. It fosters several technological concepts, one of these is the Internet of Things (IoT). IoT technology is based on a big network of machines, objects, or people called "things" interacting together to achieve a common goal. These things are continuously generating vast amounts of data. Data understanding, processing, securing and storing are significant challenges in the IoT technology which restricts its development. This paper presents a new reference IoT model for future smart IoT solutions called Cloud Web of Things (CloudWoT). CloudWoT aims to overcome these limitations by combining IoT with edge computing, semantic web, and cloud computing. Additionally, this work is concerned with the security issues which threatens data in IoT application domains.

Production systems are undergoing transitions towards cyber-physical production systems (CpPS), combining production devices with smart devices and interconnecting systems with outside networks, often referred to as Industry4.0 or smart manufacturing. This paper presents an engine testing systems in a real world industry setting and remote and predictive maintenance as a usage scenario, in which we discuss different designs for safe, secure and reliable Machine-to-Machine connectivity for CPPS. The chosen design is discussed in detail and an outlook of further applications is presented.

Industry 4.0 considers integration of IT and control systems with physical objects, software, sensors and connectivity in order to optimize manufacturing processes. It provides advanced functionalities in control and communication for an infrastructure that handles multiple tasks in various locations automatically. Automatic actions require information from trustworthy sources. Thus, this work is focused on how to ensure trustworthy communication from the edge devices to the backend infrastructure. We derive a meta-model based on RAMI 4.0, which is used to describe an end-to-end communication use case for an Industry 4.0 application scenario and to identify dependabilities in case of security challenges. Furthermore, we evaluate secure messaging protocols and the integration of Trusted Platform Module (TPM) as a root of trust for dataexchange. We define a set of representative measurable indicator points based on existing standards and use them for automated dependability detection within the whole system.

The evolution of Cyber-physical Systems and their often critical roles in many application domains such as automotive, aeronautics, energy, and railway make it necessary to address safety and security issues equally throughout the entire system lifecycle. In the past, safety and security development has been mostly performed independently. With increasing complexity and connectivity, this separation is no longer justifiable. This paper proposes a combined safety and security development lifecycle. We review existing standards in order to identify safety and security core activities. Based on the results, a combined lifecycle is introduced that integrates both safety and security considerations and activities in a coordinated way. Finally the feasibility of the approach is demonstrated by case studies.