[论文] A Hidden Markov Preference Model for Online Prediction of Adversary Ac...

论文概要

研究领域: ML 作者: Angel Daruna, Michael Cogswell, Tixiao Shan, Yi Yao, Han-Pang Chiu, Ajay Divakaran 发布时间: 2026-11-05 arXiv: 2611.08445

中文摘要

防御者要预判攻击者，需要在动作发生前基于可用信息得到攻击者下一步动作的概率分布。两个结构使问题复杂化：攻击者的行为模式会持续存在且在一个 episode 内可以切换；每个动作由操作和依赖于状态的参数组合而成，其可行值随时间变化。我们用隐马尔可夫模型同时捕捉这两个结构：隐状态为当前活跃模式，模式条件下的动作律定义在可行性掩码的可执行动作树上。同一个似然函数既构成下一动作分布，又在动作被观测后更新模式信念；部分观测的前缀贡献其已解析的局部因子。估计过程分解为凸 logistic 拟合，在模式无标签时由 Baum-Welch 算法复用。我们推导了 reach 加权的节点级 KL 表达式来刻画模式信息量、切换模式路径的序列级对数损失界、以及一致的回退保证。在 CAGE 2 网络防御基准上，滤波隐状态每步价值 0.8 nats；控制器切换后，损失在 3 步内开始下降，而循环基线需要 10 步。

原文摘要

A defender anticipating an attacker needs a probability distribution over the attacker's next action from information available before that action occurs. Two structures complicate this: the attacker's behavioral regime persists and can switch within an episode, and each action combines an operation with state-dependent arguments whose feasible values change over time. We capture both with a hidden Markov model whose latent state is the active regime and whose regime-conditioned action law is defined on a feasibility-masked tree of executable actions. The same likelihood forms the next-action distribution and updates the regime belief once the action is seen, and a partially observed prefix contributes its resolved local factors. Estimation separates into convex logistic fits that Baum-Welch reuses when regimes are unlabeled. We derive a reach-weighted nodewise KL expression for regime information, a sequence-wise log-loss bound for switching regime paths, and a uniform fallback guarantee. On the CAGE 2 cyber defense benchmark the filtered latent state is worth 0.8 nats per step, and after a controller switch the loss begins to fall in three steps against ten for a recurrent baseline.

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*自动采集于 2026-11-08*

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