Online Learning in Stackelberg Security Games with Adaptive Attacker Sequences and Time-Varying Attack Intensities
2026-08-03 • Computer Science and Game Theory
Computer Science and Game Theory
AI summaryⓘ
The authors study a kind of security game where attackers can choose multiple targets and attack strengths change over time. They create a mathematical model to predict attacker behavior and develop learning algorithms that improve decision-making with experience. Their approach works well both when all attack information is known and when only limited feedback is available. Simulations show their methods perform reliably in different scenarios.
Stackelberg Security Gamesno-regret learningmixed-integer linear programmingFollow-the-Perturbed-Leaderbandit feedbackregret boundsbarycentric spanneronline learning
Authors
Guanda Chen, Shiheng Zhang, Yue Wang, Yiding Ji
Abstract
This work studies no-regret online learning in Repeated Stackelberg Security Games with time-varying attack intensities. We formulate an extended security game in which an attacker may select multiple targets and derive an exact mixed-integer linear programming oracle under a optimistic tie-breaking rule. Under full-information feedback, the oracle is integrated with Follow-the-Perturbed-Leader and yields expected $\mathcal{O}(\sqrt{T})$ regret against non-anticipating sequences with time-varying follower numbers, attack intensities, and attacker types. Under bandit feedback, we consider multiple followers sharing a fixed attacker type and use a barycentric-spanner construction to reconstruct utility estimates from aggregate attack observations, obtaining expected $\mathcal{O}(T^{2/3})$ regret. Extensive simulations demonstrate the robustness and effectiveness of our approach under full and partial information feedback.