# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 2 Background and Related Works
### Digital Twin Applications in Cybersecurity  
While DTs have yet to be extensively applied to cybersecurity challenges, existing research underscores their potential as an effective method [21; 39]. Originating from the manufacturing sector, DTs offer a familiar framework for manufacturing engineers to address cybersecurity issues, while the insights generated by DT models provide actionable intelligence for cybersecurity engineers [40]. [PERSON] and [PERSON] [41] pioneered the integration of DTs into information security, proposing a CPS twinning model that leverages standardized data formats like Automation ML for efficient simulation environment construction. This approach facilitates automatic acquisition of the data necessary for generating DT models, while also incorporating safety and security rules to detect potential intrusions by comparing commands between senders and receivers. Building on this foundation, subsequent work by [PERSON] and [PERSON] [42] enhanced the model to include real-time data from physical systems, enabling accurate virtual mirroring and state transition monitoring. This extended model proved effective in detecting intrusions, including man-in-the-middle and insider attacks, demonstrating DT's potential as a robust platform for intrusion detection. Figure 2: Example of vulnerability chains in CPPS. [PERSON] et al. [43] further applied AML to model cyber-physical systems and conducted functional safety and cybersecurity analyses using DTs. In the 'CyberFactory#1' project, [PERSON] et al. [44] explored DT's application in assessing production system responses to cyber-attacks and predicting potential damages, although detailed outcomes were not disclosed. [PERSON] et al. [45] investigated the development of cost-effective, reliable, and security-oriented DT models, suggesting the value of creating purpose-specific multi-view DTs. [PERSON] et al. [46] introduced the concept of gamification for DT security, adopting an offensive security stance. This innovative approach transforms DT into a versatile platform that not only facilitates a learning environment geared toward enhancing security awareness but also supports automated security evaluations and offers transparent DT assessments for security analysts. This is achieved by seamlessly incorporating machine learning technologies. Additionally, DTs have been utilized beyond production systems, such as in safeguarding user privacy in smart automotive systems. [PERSON] [47] developed a DT model for smart cars to analyze operational, safety, and privacy data, employing data anonymization to mitigate privacy risks. Although interactions across the physical, digital, and human domains are increasing, research exploring the application of DT technology across diverse architectural archetypes, particularly for managing numerous unique assets, remains limited [20]. Thus, developing a comprehensive reference architecture for leveraging DT to support smart manufacturing is necessary. Toward this direction, [PERSON] et al. [48] redefined their enterprise architecture approach to depict a cooperative intelligent transport system scenario, evolving it into a threat-focused DT. This innovative shift was guided by the reference architecture model for Industry 4.0 (RAMI 4.0), facilitating a comprehensive depiction of the system's lifecycle. [PERSON] et al. [49] introduced a DT-based reference model that incorporates an information framework to depict the physical specifications and a data processing module to generate real-time representations of physical objects. [PERSON] et al. [16] proposed a framework-oriented DT architecture to support cyber-attack detection in CPPS. Additionally, an experimental case study is conducted on off-the-shelf 3D printers to illustrate the effectiveness of the proposed DT framework in detecting cyber-attacks. Nevertheless, there is a limited effort in integrating DTs into cybersecurity assessments, especially in the area of mitigation prioritization [15].