Perimeter Defense

Perimeter defense research focuses on optimizing strategies for securing boundaries, whether physical or virtual, against intrusions. Current efforts concentrate on developing efficient algorithms, including reinforcement learning (both model-based and model-free), graph neural networks, and multi-agent systems, to dynamically adapt defense strategies based on real-time information and heterogeneous conditions. These advancements aim to improve resource allocation, enhance situational awareness (especially in partially observable environments), and ultimately increase the effectiveness of perimeter security in various applications, from urban traffic management to cybersecurity. The integration of traditional control methods with machine learning techniques shows promise for achieving robust and scalable solutions.