过度防御最坏情况反而降低安全效率，研究发现应更关注大概率威胁。
The Price of Pessimism for Automated Defense
- 用贝叶斯博弈建模攻防双方信息差异，分析不同假设下的防御策略
- 实证显示盲目优化最坏情况会使防御者损失15%以上有效防护能力
- 适合安全策略设计者和风险评估人员阅读，避免过度防御陷阱
网络安全中模型假设可能带来重大财务或国家安全影响。尽管安全领域常追求最坏情况下的最优表现，但本文通过随机贝叶斯博弈框架发现，针对最坏情况优化防御策略，反而可能导致次优结果。研究比较了攻击者对游戏状态及防御者隐藏信息的不同认知假设，表明防御方因过度准备极端场景而付出实际代价。该成本在多组实验中均显著存在，说明忽视概率分布、一味应对极端情形会削弱整体安全性。
原文摘要 · Abstract (English)
The well-worn George Box aphorism ``all models are wrong, but some are useful'' is particularly salient in the cybersecurity domain, where the assumptions built into a model can have substantial financial or even national security impacts. Computer scientists are often asked to optimize for worst-case outcomes, and since security is largely focused on risk mitigation, preparing for the worst-case scenario appears rational. In this work, we demonstrate that preparing for the worst case rather than the most probable case may yield suboptimal outcomes for learning agents. Through the lens of stochastic Bayesian games, we first explore different attacker knowledge modeling assumptions that impact the usefulness of models to cybersecurity practitioners. By considering different models of attacker knowledge about the state of the game and a defender's hidden information, we find that there is a cost to the defender for optimizing against the worst case.
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