Adversarial Path Planning for Optimal CCTV Surveillance: A Case Study on Nuclear Facility Security Optimization

The security of critical infrastructure, particularly nuclear facilities, is paramount in mitigating potential threats and ensuring public safety. Conventional CCTV surveillance deployment relies on static placement strategies that fail to account for dynamic adversarial behavior, leading to coverage gaps and surveillance inefficiencies. This study proposes a novel Adversarial Path Planning (APP) framework, which integrates game-theoretic modeling, probabilistic risk assessment, and bilevel optimization to enhance surveillance coverage, intrusion detection, and resource allocation. By simulating adversarial movement patterns and iteratively refining camera placement, APP dynamically adjusts surveillance strategies to counter evolving threats while minimizing blind spots and optimizing detection probability. The APP framework models the facility as a weighted surveillance graph, identifying high-risk intrusion paths and optimizing camera positioning to maximize coverage while minimizing redundancy. A case study conducted on a hypothetical nuclear power plant demonstrates APP’s effectiveness in enhancing security resilience, achieving 95% surveillance coverage, improving detection accuracy to 98%, and reducing dead zones by 85%—significantly outperforming conventional methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Additionally, APP reduces the required number of cameras by 40% while improving cost efficiency by 27%, highlighting its potential for resource-conscious security optimization. The findings establish APP as a scalable and computationally efficient surveillance optimization solution, adaptable to nuclear security, border surveillance, and high-risk critical infrastructure protection. Future research should explore AI-driven real-time threat detection, autonomous security drones, and deep reinforcement learning-based surveillance adaptation to further enhance threat responsiveness and situational awareness in evolving security environments.