A novel probabilistically timed dynamic model for physical security attack scenarios on critical infrastructures

tThis study proposes a novel probabilistically timed dynamic model for physical securityattack scenarios on critical infrastructures (CIs). The model simulates attacker’s attemptsto compromise exploitable vulnerabilities in targeted CIs. Attacker’s times to successfullycompromise physical barriers, intrusion detection systems, and standby safety systemsare modeled as random variables represented by user-defined probability distributions. Themodel assumes a highly skilled attacker, tracks his cumulative time to compromise targetedassets relative to an estimated mission time, and calculates mission success probabilityunder imperfect information. The model uses Monte Carlo sampling technique to propa-gate uncertainties of input parameters to calculate statistics of mission success probability.Model’s utility is demonstrated by a postulated case study in which an attacker attemptsto launch undetected and unmitigated fire in 1-out-of-4 protected areas within a chemicalprocess plant. Destroying one of these protected areas represents attacker’s mission successin disrupting plant operation in addition to causing property damage. Visual flowchartingand dynamic attack tree logic are used to describe systematic execution of the attack. Simu-lation results show 64.4% mission success probability with 4.7% standard deviation. Benefitsof proposed model include its use in security training to quantify probabilistic outcomes of“what if” scenarios, uncover exploitable vulnerabilities, and implement defensive strategiesto improve CI’s resilience under attack. The modeling framework can be extended to cybersecurity applications