The security of critical infrastructure, particularly nuclear facilities, is paramount for public safety. Conventional Closed-Circuit Television (CCTV) surveillance relies on static camera placement, which fails to adapt to dynamic adversarial behavior, resulting in coverage gaps and inefficiencies. This study proposes a novel Adversarial Path Planning (APP) framework that integrates game theory, probabilistic risk assessment, and bilevel optimization to enhance surveillance coverage, intrusion detection, and resource allocation. APP simulates adversarial movement and iteratively refines camera placement to dynamically counter evolving threats, minimizing blind spots and optimizing detection probability. The framework models the facility as a weighted surveillance graph, identifying high-risk intrusion paths to optimize camera positioning for maximum coverage with minimal redundancy. A case study on a hypothetical nuclear power plant demonstrates APP’s effectiveness: it achieves 95% surveillance coverage, improves detection accuracy to 98%, and reduces dead zones by 85%, significantly outperforming conventional methods like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Furthermore, APP reduces the required number of cameras by 40% while improving cost efficiency by 27%, underscoring its potential for resource-conscious security optimization. The findings establish APP as a scalable and computationally efficient solution adaptable to nuclear facilities, border surveillance, and other high-risk critical infrastructure. Future research should explore AI-driven real-time threat detection and autonomous surveillance systems to further enhance responsiveness in dynamic security environments.
Salman et al. (Fri,) studied this question.