A bi-level game-theoretic framework for timing and selecting cyber deception against biased attackers, with simulated evidence that optimal switching yields at least a 40% reward improvement.
PsybORG+: Modeling and Simulation for Detecting Cognitive Biases in Advanced Persistent Threats
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abstract
Advanced Persistent Threats (APTs) bring significant challenges to cybersecurity due to their sophisticated and stealthy nature. Traditional cybersecurity measures fail to defend against APTs. 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.
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Bi-Level Game-Theoretic Planning of Cyber Deception for Cognitive Arbitrage
A bi-level game-theoretic framework for timing and selecting cyber deception against biased attackers, with simulated evidence that optimal switching yields at least a 40% reward improvement.