Multilayer network models (MLNs) for high consequence facilities and critical assets have been proposed to better capture the complexity, dynamism, and interdependencies of current—and anticipated—security operations in high consequence facilities (HCFs). In order to study these models, researchers at Sandia National Laboratories have created a coupled MLN and agent based real-time HCF security system model. Monte Carlo experiments have been run to understand different aspects of the security system. A central goal in the design of both the coupled model and the performed experiments was to better study system resilience. Resilience is the ability for a system to absorb, adapt and overcome a shock or targeted attack, and is a critical aspect of security systems. Multilayer network models readily provide tools for studying system resilience. Specifically, much work has been done modeling resilience of critical infrastructure and human-interaction networks, mostly limited to the single layer case. In this paper we propose a novel method for analyzing the absorptive capacity of multilayer networks which makes minimal assumptions on the networks behavior or structure and use it to analyze experimental results from the coupled models.
Year
2024
Abstract