The Estimate of Adversary Sequence Interruption (EASI) model is a single path analysis model to calculate the Probability of Interruption (PvI) of a Physical Protection System (PPS) in a facility. However, the PvI value estimated by the EASI model does not have uncertainty value which is important to represent the confidence level of the PPS's performance. A stochastic (Monte Carlo) approach to analyze the effectiveness of a PPS, specifically estimating the PvI value and uncertainty in the PvI estimation, is implemented into the EASI model approach in a software code developed as part of this study. The software code is tested by analyzing a hypothetical facility by estimating PvI values considering the characteristics [Probability of Detection (PvD) and delay time (tvd)] of the protection elements in the PPS, uncertainties in the PvD and tvD values, and various adversary strategies including collusion with an insider. Sensitivity analysis of PvI value with regards to PvD and tvd values is performed for the Most Vulnerable Path (MVP) of the facility by considering the Critical Detection Point (CDP) of the facility's Adversary Sequence Diagram (ASD). Sensitivity analysis of PvI value estimation shows that the relationship between PvD and PvI is linear however the relationship between tvd and PvI is non-linear. The implementation of stochastic (Monte Carlo) approach successfully produces PvI value distribution from which the mean and standard deviation values are estimated. The PvI value is the lowest in the simulations where the insider's act is included, whether the insider acts on the detection or delay function or both simultaneously. The lowest mean value of PvI distribution is for the rushing strategy, among the other adversary strategies analyzed. This is due to an unbalanced PPS design of the hypothetical facility analyzed. Frequency analysis of PvI value also shows that simulations of rushing strategy have a higher frequency of lower PvI value (below 0.8) compared to the other strategies. In conclusion, the implementation of the stochastic (Mote Carlo) method is valuable in modeling the PvD values in the EASI model, and in the estimation of PvI value distribution and the uncertainty associated, especially in modeling the adversary path including the collusion of an insider for multi-path analysis. Frequency analysis performed on the PvI values is valuable in modifying the PPS design instead of just using the mean value of the PvI distribution and its standard deviation in the multi-path analysis.
The Estimate of Adversary Sequence Interruption (EASI) model is a single path analysis model to calculate the Probability of Interruption (PvI) of a Physical Protection System (PPS) in a facility. However, the PvI value estimated by the EASI model does not have uncertainty value which is important to represent the confidence level of the PPS's performance. A stochastic (Monte Carlo) approach to analyze the effectiveness of a PPS, specifically estimating the PvI value and uncertainty in the PvI estimation, is implemented into the EASI model approach in a software code developed as part of this study. The software code is tested by analyzing a hypothetical facility by estimating PvI values considering the characteristics [Probability of Detection (PvD) and delay time (tvd)] of the protection elements in the PPS, uncertainties in the PvD and tvD values, and various adversary strategies including collusion with an insider. Sensitivity analysis of PvI value with regards to PvD and tvd values is performed for the Most Vulnerable Path (MVP) of the facility by considering the Critical Detection Point (CDP) of the facility's Adversary Sequence Diagram (ASD). Sensitivity analysis of PvI value estimation shows that the relationship between PvD and PvI is linear however the relationship between tvd and PvI is non-linear. The implementation of stochastic (Monte Carlo) approach successfully produces PvI value distribution from which the mean and standard deviation values are estimated. The PvI value is the lowest in the simulations where the insider's act is included, whether the insider acts on the detection or delay function or both simultaneously. The lowest mean value of PvI distribution is for the rushing strategy, among the other adversary strategies analyzed. This is due to an unbalanced PPS design of the hypothetical facility analyzed. Frequency analysis of PvI value also shows that simulations of rushing strategy have a higher frequency of lower PvI value (below 0.8) compared to the other strategies. In conclusion, the implementation of the stochastic (Mote Carlo) method is valuable in modeling the PvD values in the EASI model, and in the estimation of PvI value distribution and the uncertainty associated, especially in modeling the adversary path including the collusion of an insider for multi-path analysis. Frequency analysis performed on the PvI values is valuable in modifying the PPS design instead of just using the mean value of the PvI distribution and its standard deviation in the multi-path analysis.