Dynamic Shields: A Game-Theoretic Reinforcement Learning Framework for APT Mitigation

doi:10.1007/978-3-032-08064-6_7

Dynamic Shields: A Game-Theoretic Reinforcement Learning Framework for APT Mitigation

2026-01-01

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DOI：10.1007/978-3-032-08064-6_7

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Advanced Persistent Threats (APTs), exemplified by the SolarWinds attack, demand adaptive defenses in partially observable network environments. We model the defender-attacker interaction as a Partially Observable Markov Decision Process (POMDP)-based stochastic game, executed in the test environment AttackBed, and solved using reinforcement learning (RL) with Proximal Policy Optimization (PPO) and Recurrent PPO (RPPO). Our contributions include: (1) theorems proving equilibrium existence, threshold-structured best responses, and convergence properties, (2) a high-fidelity GNS3-based simulation aligned with MITRE ATT&CK/D3FEND frameworks, and (3) empirical comparisons showing PPO outperforms RPPO in mitigating attacks. PPO reduces attack success rates by 65%, leveraging sample efficiency in realistic settings. This comprehensive study advances game-theoretic RL for cyber defense, providing a foundation for future multi-agent frameworks.

Keywords:

Reinforcement Learning

Game Theory

Cybersecurity

Advanced PersistentThreats

POMDP

ThresholdPolicy

Dynamic Shields: A Game-Theoretic Reinforcement Learning Framework for APT Mitigation

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