# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 1 Introduction  
The integration of cyber and physical systems has profoundly transformed the manufacturing sector, leading to the development of cyber-physical production systems (CPSs) and driving a shift toward intelligent manufacturing [1]. CPPSs, renowned for their adaptive capabilities to varying operational contexts, have yielded substantial enhancements in production processes, thereby augmenting efficiency and productivity [1]. Nonetheless, this integration also introduces increasing cybersecurity vulnerabilities, as underlined by incidents such as the \"WannaCry\" ransomware attack in 2017 [2]. Moreover, vulnerabilities often remain undetected until they are exploited in such environments. Even when vulnerabilities are identified and reported in databases like the common vulnerabilities and exposures (CVE), they may not be promptly addressed due to various operational constraints [3, 4]. Cybersecurity challenges in information technology (IT) and smart manufacturing share some similarities but exhibit distinct approaches due to the unique nature of the systems they protect and the threats they encounter [5]. While IT security primarily focuses on safeguarding digital assets such as data and cloud services, smart manufacturing security extends its scope to protect operational technology (OT) systems, such as industrial control systems and physical machinery [6]. The convergence of IT and OT systemspresents significant complexities in asset management and dependence analysis, posing significant challenges to vulnerability mitigation [7; 8]. A primary concern in IT security is the identification and prioritization of vulnerable assets, which is complicated by the sheer volume of components and their intricate interconnections. This challenge is further magnified in the OT domain, where maintaining continuous production often takes higher priority over asset scanning and vulnerability patching, as the latter may pose operational disruption risks [9; 10]. This difficulty is heightened in IT and OT converged environments, like CPPSs, where gaining a comprehensive understanding is crucial for systematically assessing vulnerabilities [11; 12]. Moreover, the threat landscapes faced by IT security and smart manufacturing security differ significantly. IT security contends with cyber threats, like malware, phishing, and data breaches, while smart manufacturing security faces unique challenges such as sabotage, physical tampering, and supply chain attacks. Consequently, IT security emphasizes data confidentiality, integrity, and compliance with regulations, while smart manufacturing prioritizes operational continuity, safety, and reliability [13]. Digified twin (DT) [14]-based methods emerge as a solution to address the gaps of asset management complexities [9; 10] and vulnerable component patch prioritization [15], while protecting the monitoring of industrial assets [16; 17]. DT methods, by functioning as virtual replicas of physical components, offer critical insights by aggregating asset-specific data and enabling analytics [18; 19], or act as service providers through additive manufacturing [20]. Furthermore, they support enterprise security efforts by simulating attacks and evaluating potential impacts on virtual counterparts [21]. However, the adoption of DT security simulations within broader enterprise security frameworks, typically overseen by security operations centers, remains largely unexplored and underutilized [22]. This paper contributes to the field by presenting a DT-centered security framework tailored to enhance asset visibility and prioritize mitigation in CPPS. Our framework-oriented methodology consists of three key modules: a reference architecture that represents various unique CPPS assets; dependence rules within cyber-physical layers that facilitate component criticality analysis; and a virtual patch tuning and component vulnerability score calculation algorithm that enables patch prioritization.