As the threat landscape in the industrial domain continually advances, security-by-design is an ever-growing concern in the engineering of cyber-physical production systems (CPPSs). Often, quality aspects are not considered when securing CPPSs, which creates attack vectors that could lead to malicious activity affecting the products' quality. Since quality control systems generally provide inadequate protection against intentionally introduced defects, and can be susceptible to attacks, quality considerations must be integrated into security-aware CPPS engineering. For this purpose, we propose the QualSec method that automatically identifies security risks pertaining to CPPSs, building on the quality characteristics associated with manufacturing operations to determine cascading effects. QualSec is based on a semantic representation of engineering knowledge, allowing to efficiently reuse engineering models from AutomationML artifacts. Moreover, QualSec utilizes Petri nets to facilitate the analysis of security risks and cascading effects. In this way, QualSec informs users about possible attack paths for compromising quality characteristics, how attackers may disguise their malicious actions, and the possible consequences of attacks with respect to product quality. We demonstrate the benefits of QualSec in a case study and analyze its scalability through a rigorous performance evaluation.

Systems integrators and vendors of industrial components need to establish a security-by-design approach, which includes the assessment and subsequent treatment of security risks. However, conducting security risk assessments along the engineering process is a costly and labor-intensive endeavor due to the complexity of the system(s) under consideration and the lack of automated methods. This, in turn, hampers the ability of security analysts to assess risks pertaining to cyber-physical systems (CPSs) in an efficient manner. In this work, we propose a method that automatically identifies security risks based on the CPS's data representation, which exists within engineering artifacts. To lay the foundation for our method, we present security-focused semantics for the engineering data exchange format AutomationML (AML). These semantics enable the reuse of security-relevant know-how in AML artifacts by means of a formal knowledge representation, modeled with a security-enriched ontology. Our method is capable of automating the identification of security risk sources and potential consequences in order to construct cyber-physical attack graphs that capture the paths adversaries may take. We demonstrate the benefits of the proposed method through a case study and an open-source prototypical implementation. Finally, we prove that our solution is scalable by conducting a rigorous performance evaluation.

Operators of cyber-physical systems (CPSs) need to maintain awareness of the cyber situation in order to be able to adequately address potential issues in a timely manner. For instance, detecting early symptoms of cyber attacks may speed up the incident response process and mitigate consequences of attacks (e.g., business interruption, safety hazards). However, attaining a full understanding of the cyber situation may be challenging, given the complexity of CPSs and the ever-changing threat landscape. In particular, CPSs typically need to be continuously operational, may be sensitive to active scanning, and often provide only limited in-depth analysis capabilities. To address these challenges, we propose to utilize the concept of digital twins for enhancing cyber situational awareness. Digital twins, i.e., virtual replicas of systems, can run in parallel to their physical counterparts and allow deep inspection of their behavior without the risk of disrupting operational technology services. This paper reports our work in progress to develop a cyber situational awareness framework based on digital twins that provides a profound, holistic, and current view on the cyber situation that CPSs are in. More specifically, we present a prototype that provides real-time visualization features (i.e., system topology, program variables of devices) and enables a thorough, repeatable investigation process on a logic and network level. A brief explanation of technological use cases and outlook on future development efforts completes this work.

Blockchain has been hailed as an emerging technology with the potential to cause significant impact in a variety of fields. One domain is an application in cyber-physical systems (CPSs), e.g., as a building block for the (Industrial) Internet of Things (IIoT) and Industry 4.0. In this regard, various use cases and designs have been proposed that seek to leverage the desirable properties blockchain technologies seem to offer. While many of the principles behind blockchain have actually been studied under the veil of Byzantine fault tolerance for decades, some approaches, such as relying on game-theoretic incentives and proof-of-work (PoW), are not yet fully understood. This knowledge gap can leave both practitioners and researchers in a difficult position regarding a possible application of such technologies, as it is often unclear what guarantees and characteristics a particular blockchain design actually achieves. This work-in-progress paper provides an overview of blockchain security research, outlines system designs that are likely to exhibit vulnerabilities, and provides examples of potentially insecure proposals in the field of CPSs that employ such designs.

As the connectivity within manufacturing processes increases in light of Industry 4.0, information security becomes a pressing issue for product suppliers, systems integrators, and asset owners. Reaching new heights in digitizing the manufacturing industry also provides more targets for cyber attacks, hence, cyber-physical production systems (CPPSs) must be adequately secured to prevent malicious acts. To achieve a sufficient level of security, proper defense mechanisms must be integrated already early on in the systems' lifecycle and not just eventually in the operation phase. Although standardization efforts exist with the objective of guiding involved stakeholders toward the establishment of a holistic industrial security concept (e.g., IEC 62443), a dedicated security development lifecycle for systems integrators is missing. This represents a major challenge for engineers who lack sufficient information security knowledge, as they may not be able to identify security-related activities that can be performed along the production systems engineering (PSE) process. In this paper, we propose a novel methodology named Security Development Lifecycle for Cyber-Physical Production Systems (SDL-CPPS) that aims to foster security by design for CPPSs, i.e., the engineering of smart production systems with security in mind. More specifically, we derive security-related activities based on (i) security standards and guidelines, and (ii) relevant literature, leading to a security-improved PSE process that can be implemented by systems integrators. Furthermore, this paper informs domain experts on how they can conduct these security-enhancing activities and provides pointers to relevant works that may fill the potential knowledge gap. Finally, we review the proposed approach by means of discussions in a workshop setting with technical managers of an Austrian-based systems integrator to identify barriers to adopting the SDL-CPPS.