论文标题
基于模型的，基于自动网络响应的决策理论观点
A Model-Based, Decision-Theoretic Perspective on Automated Cyber Response
论文作者
论文摘要
网络攻击可能会以机器速度发生得太快的机器速度，对于人类的（或有时是在线）决策而言，无法成为可行的选择。尽管人类的投入仍然很重要，但在这种情况下，防御性人工智能（AI）系统必须具有相当大的自主权。当AI系统基于模型时，其行为响应可以与风险感知的成本/收益折衷方案对齐，这些偏好定义为用户提供的偏好，这些偏好捕获了人类操作员如何理解系统，对手和任务的关键方面。本文介绍了一种沿着这些线路设计的自动网络响应的方法。我们将对系统的模拟与任何时间的在线规划师结合在一起，以解决以部分可观察到的马尔可夫决策问题（POMDP）的特征的网络防御问题。
Cyber-attacks can occur at machine speeds that are far too fast for human-in-the-loop (or sometimes on-the-loop) decision making to be a viable option. Although human inputs are still important, a defensive Artificial Intelligence (AI) system must have considerable autonomy in these circumstances. When the AI system is model-based, its behavior responses can be aligned with risk-aware cost/benefit tradeoffs that are defined by user-supplied preferences that capture the key aspects of how human operators understand the system, the adversary and the mission. This paper describes an approach to automated cyber response that is designed along these lines. We combine a simulation of the system to be defended with an anytime online planner to solve cyber defense problems characterized as partially observable Markov decision problems (POMDPs).
