PsybORG+: Modeling and Simulation for Detecting Cognitive Biases in Advanced Persistent Threats
Shuo Huang, Fred Jones, Nikolos Gurney, David V. Pynadath, Kunal Srivastava, Stoney A. Trent, Peggy Wu, Quanyan Zhu · 2024
Advanced Persistent Threats (APTs) bring significant challenges to cybersecurity due to their sophisticated and stealthy nature. Cognitive vulnerabilities can significantly influence attackers’ decision-making processes, which presents an opportunity for defenders to exploit. This work introduces PsybORG+, a multi-agent cybersecurity simulation environment designed to model APT behaviors influenced by cognitive vulnerabilities. A classification model is built for cognitive vulnerability inference and a simulator is designed for synthetic data generation. Results show that PsybORG+can effectively model APT attackers with different loss aversion and confirmation bias levels. The classification model has at least a 0.83 accuracy rate in predicting cognitive vulnerabilities.