Vulnerability learning of adversary paths in Physical Protection Systems using AMC/EASI

• DOI: 10.1016/j.pnucene.2021.103666
• Publication Date: 2021-02-05T02:37:33Z
• Authors: Bowen ZOU; Mengkun LI; Ming YANG
• Keywords: Vulnerability; Sequence (biology)

Abstract This paper presents a vulnerability learning method for the analysis of adversary paths in Physical Protection System (PPS) named AMC/EASI. AMC/EASI is used absorbing Markov chains (AMC) for the establishment and simulation of state chains and used Estimate of Adversary Sequence Interruption (EASI) method for the calculation of the probability of transitions. Moreover, this paper maps the adversary sequence diagram into the absorbing Markov chain. According to the AMC, the detailed analysis processes for predicting the expected number of the defeated protection elements and the expected value of the intrusion path length are presented. Also, this paper proposed a sensitivity analysis approach for the protection elements by AMC/EASI and the selectivity analysis of multi-targets. Thus, AMC/EASI provides more guidance for the design, reconstruction, and evaluation of PPS.