# Strategic Cyber Defense via Reinforcement Learning-Guided Combinatorial Auctions

arXiv: https://arxiv.org/abs/2509.10983 · HTML: https://mlsanity.com/paper/2509.10983/

## 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.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 12 of 12)

- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [CybORG: A Gym for the Development of Autonomous Cyber Agents](https://mlsanity.com/paper/2108.09118/) (2021)
- [A Permutation-Equivariant Neural Network Architecture For Auction Design](https://mlsanity.com/paper/2003.01497/) (2020)
- [Network Environment Design for Autonomous Cyberdefense](https://mlsanity.com/paper/2103.07583/) (2021)
- [Optimal-er Auctions through Attention](https://mlsanity.com/paper/2202.13110/) (2022)
- [On Autonomous Agents in a Cyber Defence Environment](https://mlsanity.com/paper/2309.07388/) (2023)
- [A Scalable Neural Network for DSIC Affine Maximizer Auction Design](https://mlsanity.com/paper/2305.12162/) (2023)
- [Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies](https://mlsanity.com/paper/2211.15557/) (2022)
- [Automated Cyber Defence: A Review](https://mlsanity.com/paper/2303.04926/) (2023)
- [Autonomous Network Defence using Reinforcement Learning](https://mlsanity.com/paper/2409.18197/) (2024)
- [Advancing Differentiable Economics: A Neural Network Framework for Revenue-Maximizing Combinatorial Auction Mechanisms](https://mlsanity.com/paper/2501.19219/) (2025)
- [Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platforms](https://mlsanity.com/paper/2506.08192/) (2025)

## 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.