mlsanityData to 14 Oct 2026

Strategic Cyber Defense via Reinforcement Learning-Guided Combinatorial Auctions

arXiv 2509.10983 ↗First posted 13 Sep 2025cs.GTcited by 0

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

Abstract (arXiv)

Cyber defense operations increasingly require long-term strategic planning under uncertainty and resource constraints. We propose a new use of combinatorial auctions for allocating defensive action bundles in a realistic cyber environment, using host-specific valuations derived from reinforcement learning (RL) Q-values. These Q-values encode long-term expected utility, allowing upstream planning. We train CAFormer, a differentiable Transformer-based auction mechanism, to produce allocations that are approximately incentive-compatible under misreporting. Rather than benchmarking against existing agents, we explore the qualitative and strategic properties of the learned mechanisms. Compared to oracle and heuristic allocations, our method achieves competitive revenue while offering robustness to misreporting. In addition, we find that allocation patterns correlate with adversarial and defensive activity, suggesting implicit alignment with operational priorities. Our results demonstrate the viability of auction-based planning in cyber defense and highlight the interpretability benefits of RL-derived value structures.

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 12 of its 28 references that are in the map. First posted on arXiv 13 Sep 2025.

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12 of 28 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platforms9 Jun
  2. 2024Autonomous Network Defence using Reinforcement Learning26 Sep

Other references · 10

  1. 2019Deep Reinforcement Learning for Cyber Securitycited by 95
  2. 2021CybORG: A Gym for the Development of Autonomous Cyber Agentscited by 60
  3. 2020A Permutation-Equivariant Neural Network Architecture For Auction Designcited by 32
  4. 2021Network Environment Design for Autonomous Cyberdefensecited by 32
  5. 2022Optimal-er Auctions through Attentioncited by 27
  6. 2023On Autonomous Agents in a Cyber Defence Environmentcited by 26
  7. 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Designcited by 24
  8. 2022Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policiescited by 23
  9. 2023Automated Cyber Defence: A Reviewcited by 21
  10. 2025Advancing Differentiable Economics: A Neural Network Framework for Revenue-Maximizing Combinatorial Auction Mechanismscited by 2

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  5. 2026Beyond Rewards in Reinforcement Learning for Cyber Defence
  6. 2025Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agents
  7. 2025PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense
  8. 2026Efficient Nash Equilibrium Computation for Cybersecurity Games