Article Details

Abstract

To address the issue that the traditional Adversary Sequence Diagram (ASD) neglects distance factors across different paths and struggle to accurately identify the most vulnerable paths in multi-asset scenarios, this paper proposes a vulnerability assessment method integrating an optimized ASD with EASI multi-path code. First, the traditional ASD is optimized by constructing a path tree based on the specific scenario. Then, the probability of interruption for the most vulnerable intrusion paths is calculated using EASI multi-path analysis code. Next, Monte Carlo simulation is applied to reduce parameter uncertainty. Finally, sensitivity analysis is conducted to identify high-risk security components and propose improvement measures. The method is applied to an industrial facility for empirical analysis. The results indicate that the probability of interruption for the most vulnerable path in the core resource processing equipment area is 84.5%, whereas it is only 68.0% for the main office area, revealing significant security risks. Additionally, the sensitivity analysis identifies high-sensitivity components such as peripheral channels. This method effectively addresses the issues of path identification and quantitative assessment in multi-asset scenarios, providing a scientific basis for decision-making in the optimization of Physical Protection Systems (PPS).

Keywords

Physical Protection System; EASI model; Monte Carlo simulation; risk value

References

1. [1] Setiawan Y A，Chirayath S S，Kitcher E D. MAPPS: A Stochastic Computational Tool for Multi-path Analysis of Physical Protection Systems [J/OL].Annals of Nuclear Energy，2020，137：107074.https://doi.org/10.1016/j.anucene.2019.107074.
2. [2] Andiwijayakusuma D，Mardhi A，Asmoro T，et al. Physical Protection System Effectiveness Calculation in Nuclear Reactor Facility Using EASI Code: Case Study Sabotage Scenario [J/OL].Journal of Physics: Conference Series，2021，2072（1）：012010.https://iopscience.iop.org/article/10.1088/1742-6596/2072/1/012010.
3. [3] Andiwijayakusuma D，Setiadipura T，Purqon A，et al. The Development of EASI-based Multi-path Analysis Code for Nuclear Security System with Variability Extension [J].Nuclear Engineering and Technology，2022，54（10）：3604-3613.
4. [4] Kampova K，Lovecek T，Rehak D. Quantitative Approach to Physical Protection Systems Assessment of Critical Infrastructure Elements: Use Case in the Slovak Republic [J/OL].International Journal of Critical Infrastructure Protection，2020，30：100376.https://www.sciencedirect.com/science/article/abs/pii/S1874548220300408.
5. [5] Yang J，HONG Y-M，Lv Y-M，et al. A Novel Heuristic Pathfinding Algorithm for 3D Security Modeling and Vulnerability Assessment [J].Nuclear Science and Techniques，2025，36（5）：10-24.
6. [6] Zou B W，Li M K，Yang M. Vulnerability Learning of Adversary Paths in Physical Protection Systems Using AMC/EASI [J/OL].Progress in Nuclear Energy，2021，134：103666.https://doi.org/10.1016/j.pnucene.2021.103666.
7. [7] Wang Xue, Liu Lina, Zang Na Vulnerability assessment of overseas campsite security system based on EASI model [J]. Technology Innovation and Application, 2024, 14 (26): 1-5
8. [8] Wang Qingqing, Wang Changwei, Fang Wentao, etc Vulnerability Analysis of Security Prevention System Based on EASI Model [J]. Safety and Environmental Engineering, 2020, 27 (6): 126-132186
9. [9] Xue Chaohui, Qian Wenhai, Chen Wenjing Analysis and Improvement of Weak Paths in Security Systems of Research Institutes Based on EASI Model [J]. Modern Information Technology, 2022, 6 (1): 116-119123
10. [10] Qian Wenhai, Zhang Bo, Zhou Jingjing, etc Evaluation of Physical Protection Efficiency of Nuclear Materials Based on Markov Chain and EASI Model [J]. Intelligent Computers and Applications, 2022, 12 (3): 97-102106
11. [11] Li Aiguo, Lei Lufei, Chen Bo, etc Dynamic evaluation method for physical protection system based on EASI model [J]. Computer Simulation, 2022, 39 (2): 235-239245
12. [12] Zhao Peixiang Security System Efficiency Analysis and Simulation Research Based on Intrusion Path Analysis [D]. Beijing: People's Public Security University of China, 2023
13. [13] Ma Haoming Effectiveness Analysis of Physical Protection System Based on 3D Simulation Modeling and Reinforcement Learning [D]. Guangzhou: South China University of Technology, 2024

About the authors

Peng Bo (2001-), male, Han ethnicity, from Suqian, Jiangsu, currently pursuing a master's degree, with a research focus on security and prevention system assessment; Zhang Mingyan (2001-), male, Han ethnicity, from Weifang, Shandong, is currently pursuing a master's degree in risk assessment and prediction; Yang Yuan (2001-), male, Han ethnicity, from Ordos, Inner Mongolia, is currently pursuing a master's degree in security and prevention system assessment.