## 4 Case Study of an IoT Enabled Bridge
### Analysis and Results  
This section provides the results obtained performing Monte Carlo simulations and a sensitivity analysis, using as variables the values in the ranges shown in Table 4. Five thousand Monte Carlo simulations were performed for each scenario using random number generators uniformly distributed. For the output (i.e., cyber-physical risk), the basic statistical measures of mean, maximum, minimum and coefficient of variation were consid \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **Vulnerability** & **Threat Source** & **Physical Impact** \\  & **(W\({}_{\text{J}}\))/(X\({}_{\text{J}}\))** & **(W\({}_{\text{J}}\))/(X\({}_{\text{J}}\))** & **(X\({}_{\text{J}}\))** \\ \hline \hline \multirow{4}{*}{1. No control barriers} & **AV**, **AC**: (VH)/(H to VH) & \multirow{4}{*}{**KN**: (H-VH)/(M-VH)} & \multirow{4}{*}{**Economic losses**} \\  & **UI**, **PR**: (VL)/(VL) & & \\  & **IO**: (VL)/(VL) & & \\  & **AV**: (VH)/(VL TO L) & & \\  & **AC**: (VH)/(H to VH) & & \\  & **UI**, **PR**: (VL)/(H to VH) & & \\  & **IO**: (VL)/(VL) & & \\  & **AV**: (VH)/(H to VH) & & \\  & **AC**: (VH)/(VL) & & \\  & **AU**: (VH)/(VL) & & \\  & **AU**: (VH)/(H to VH) & & \\  & **IC**: (VH)/(VL) & & \\  & **UI**, **PR**: (VL)/(H to VH) & & \\ \hline \hline \end{tabular}  
* **AV**: _Attack vector_, **AC**: _Attack complexity_, **UI**: _User Interaction_, **PR**: _Privielges required_, **IO**: Interoperability_, **KN**: _Knowledge_, **RE**: _Resources_, **PS**: _Psychology_, **Te**: _Terrorism experience_; **X**:VL: Very Low (0-1), L: Low (2-4), M: Medium (5-7), H: High (8-9), VH: Very High (10); Wi: VL: (0-0.20), L: (0.21-0.40), M: (0.41-0.60), H: (0.61-0.80), VH: (0.81-1.0).  
\end{table} 
Table 4: Assigned scores to vulnerability, threat source and physical impact per scenario. ered [78]. Simulation results that represent the cyber-physical risk for each scenario using the inputs from Table 4 are presented in Figure 4. It can be seen in Figure 4 that a significant reduction occurs in the cyber-physical risk when ICBs operate together (Scenario 4). Comparing Scenario 1 (i.e., No barriers) and Scenario 4 (i.e., ICBs), it was possible to obtain a decrease in risk of 62.16% in maximum values and of 71.8% in mean values. The coefficient of variation, as the relative dispersion of data points around the mean values, ranges from 15% to 18.7% for Scenarios 1, 2 and 3 indicating a smaller dispersion and greater reliability of the mean as statistical measure, as compared to 30.6% for Scenario 4. The Monte Carlo outputs in Figure 5 demonstrate the frequency of occurrence in the determined risk boundaries (see Table 2) for each scenario. Results from Figure 5 illustrate that the cyber-physical attack against the IoT based WSN can result in conditions of High Risk (i.e., risk greater than 343) occurring 76.6% in Scenario 1 (no barriers) whilst including any control barrier in the cyber and/or physical space completely eliminates completely the High risk (3.2% in Scenario 2 and 0% in Scenarios 3 and 4). Figure 4: Cyber-physical risk per scenario. Figure 5: Frequency of occurrence of cyber-physical risk levels per scenario. The inclusion of the importance indexes contributes towards the accurate calculation of cyber-physical risk, in terms of the importance of certain characteristics. Other studies or standards do not take the importance index into consideration. The influence of the importance index is investigated from the Monte Carlo simulations using the input scores from Table 4, when \(W_{i}=1\) for every characteristic. The results are illustrated in Figure 6. Results from Figure 6 demonstrate that ignoring the importance indexes, can result in a significant underestimation of cyber-physical risk.