Adaptive honeypot allocation in multi-attacker networks via Bayesian Stackelberg Games

This research paper presents a method for optimally placing honeypots (decoy systems designed to attract and monitor attackers) in networks where multiple attackers operate simultaneously, using Bayesian Stackelberg Games (a mathematical framework for strategic decision-making under incomplete information). The approach aims to help defenders allocate honeypots more effectively by predicting attacker behavior and making strategic placement decisions.