# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 4 Case Study
### Attack Simulation Using Digital-Twin Model  
Utilizing our DT model and particularly Definition 3, we can effectively simulate a range of vulnerabilities and attack scenarios, assessing their overall impact on the system. This allows us to virtually patch the system and evaluate the impact of such a patch on component risk scores using Definition 2.  
We seamlessly integrated our DT security model with a combination of open-source and commercialized tools to facilitate comprehensive attack simulations. One tool utilized is _scuriCAD_. Through this integration, the HRC model encompasses an average of 630 components and approximately 885 dependencies, spanning both physical and cyber aspects. Physical dependencies are organized by zones, while cyber dependencies rely on configuration settings and data connections. This model enables detailed simulation of attack scenarios by establishing specific entry points for attackers, which are assumed to occur with certainty. For instance, a phishing attack might involve tricking an internal user into initiating unauthorized data flow to a malicious host, identified as the primary entry point for the attack.  
The model introduces vulnerabilities through configurations of deficient defense mechanisms, assigning probabilities to evaluate the risk level of each vulnerability. For example, the minimal likelihood of a firewall's presence indicates a significant risk of access control vulnerabilities. Subsequent analysis examines how attacks spread and their ripple effects, comparing the severity of different scenarios. Key metrics for evaluation encompass the probability of an attack's success, its ramifications on production and safety, and the financial implications of defense measures, as determined by expert assessments.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Criticality** & **Number of Vulnerability** & **Average Severity** \\ \hline \(\text{HRC\_MainController\_UnitController}\) & 6 & N/A & N/A \\ \hline \(\text{HRC\_MainController\_RobotStudio}\) & 1 & 1 & 7.4 \\ \hline \(\text{HRC\_MainController\_Cockpit}\) & 2 & N/A & N/A \\ \hline \(\text{HRC\_MainController\_Drag\&Bot}\) & 2 & N/A & N/A \\ \hline \(\text{HRC\_MainController\_DockerEngineue}\) & 4 & 8 & 7.23 \\ \hline \(\text{HRC\_MainController\_OperatingSystem}\) & 5 & 19 & 7.74 \\ \hline \(\text{HRC\_WorkerIdentification\_OperatingSystem}\) & 1 & 19 & 7.74 \\ \hline \(\text{HRC\_WorkerIdentification\_WorkerIdentification}\) & 1 & N/A & N/A \\ \hline \(\text{HRC\_CollisionAvoidance\_OperatingSystem}\) & 2 & 19 & 7.74 \\ \hline \(\text{HRC\_CollisionAvoidance\_CollisionAvoidance}\) & 2 & N/A & N/A \\ \hline \(\text{HRC\_HMIC\_OperatingSystem}\) & 1 & 2 & 7.3 \\ \hline \(\text{HRC\_Router}\) & 5 & 4 & 8.08 \\ \hline \(\text{HRC\_IRC5}\) & 2 & 2 & 9.8 \\ \hline \(\text{HRC\_PLC}\) & 2 & 5 & 7.2 \\ \hline \end{tabular}
\end{table}
Table 1: Vulnerability patch decision-making considering criticality and severity.  
In this analysis, we evaluate the cascading effects of two models subjected to the same phishing attack, using attack graphs to trace the paths and vulnerability chains, as shown in Figures 10 and 11.  
In these graphs, lines with arrows denote attack trajectories, with red lines highlighting the primary path and orange lines indicating secondary or alternative paths. The model depicted in Figure 10, which employs more robust access control measures, demonstrates an average time-to-compromise (TTC) of 110 days for an attacker targeting the robot network. Conversely, the model illustrated in Figure 11, compromised by vulnerabilities such as default password settings, presents an easier target for attackers, reducing the average TTC to 68 days and thereby indicating a significantly higher risk level. Clearly, the configuration represented in Figure 10 emerges as the more secure and preferable option.