mlsanityData to 14 Oct 2026

Adaptive Learning for Moving Target defence: Enhancing Cybersecurity Strategies

arXiv 2508.17945 ↗First posted 25 Aug 2025cs.GTcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

In this work, we model Moving Target Defence (MTD) as a partially observable stochastic game between an attacker and a defender. The attacker tries to compromise the system through probing actions, while the defender minimizes the risk by reimaging the system, balancing between performance cost and security level. We demonstrate that the optimal strategies for both players follow a threshold structure. Based on this insight, we propose a structure-aware policy gradient reinforcement learning algorithm that helps both players converge to the Nash equilibrium. This approach enhances the defender's ability to adapt and effectively counter evolving threats, improving the overall security of the system. Finally, we validate the proposed method through numerical simulations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 15 references that are in the map. First posted on arXiv 25 Aug 2025.

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5 of 15 references.

References · 5

  1. 2022On the convergence of policy gradient methods to Nash equilibria in general stochastic gamescited by 16
  2. 2020Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defensecited by 15
  3. 2023Learning Near-Optimal Intrusion Responses Against Dynamic Attackerscited by 15
  4. 2019A Survey of Moving Target Defenses for Network Securitycited by 6
  5. 2022RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoTcited by 3

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  4. 2024Automated Security Response through Online Learning with Adaptive Conjectures
  5. 2025An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents
  6. 2024Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
  7. 2025Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
  8. 2024Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learning