Strategic Cyber Defense via Reinforcement Learning-Guided Combinatorial Auctions

2025-09-13

AI Summary

This work addresses long-term strategic planning for network defense under resource constraints and uncertainty. Methodologically, it proposes a reinforcement learning–guided combinatorial auction mechanism, introducing CAFormer—a differentiable Transformer architecture—for the first time into combinatorial auction design. CAFormer jointly models deep Q-values (encoding long-horizon utility) and allocation rules in an end-to-end manner, achieving approximate incentive compatibility and robustness against strategic misreporting. It further uncovers implicit alignment between defensive resource allocation patterns and adversarial dynamics. Experiments demonstrate that the method matches the revenue performance of optimal benchmarks and heuristic algorithms, significantly improves robustness to strategic bid manipulation, and yields highly interpretable allocations aligned with real-world defense prioritization requirements.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Adversarial Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Strategic cyber defense planning under uncertainty and constraints
• Allocating defensive actions via reinforcement learning-guided auctions
• Ensuring incentive-compatibility and robustness against misreporting

Innovation

• Reinforcement learning-guided combinatorial auctions
• Transformer-based differentiable auction mechanism
• Host-specific valuations from RL Q-values