Game-Theoretic Method for Optimal Initial Allocation of Honeypots for Enhanced Network Security

In this paper, we introduce a novel algorithm for the initial placement of honeypots. Our method is grounded in a two-person, zero-sum game framework that rigorously considers various factors: the cost of deploying honeypots, the effectiveness of defense strategies, the expenses borne by attackers, the consequences of successful attacks, and the value of network nodes. The cornerstone of our approach is a reward function designed to incentivize defenders to focus on securing high-value nodes, thereby enhancing overall network security. Through comprehensive simulations that account for changing target nodes and node weights, our algorithm demonstrates superior adaptability and efficacy in bolstering defenses against sophisticated cyber threats in dynamic networks such as the Internet of Battlefield Things (IoBT) networks, filling a critical void in existing security paradigms.