智能自主智能体在网络安全领域具有巨大潜力。然而，由于许多最先进的方法依赖于难以解释的黑箱模型，利益相关者越来越需要能够清晰、可操作地洞察其潜在信念和动机的方法。为解决这一问题，我们评估了用于自主网络操作的心智理论（Theory of Mind, ToM）方法。在学习到稳健的先验知识后，ToM模型仅通过少量过去的行为观察就能预测智能体的目标、行为和情境信念。本文介绍了一种专为网络防御设计的新型图神经网络（Graph Neural Network, GNN）ToM架构——图入图出（Graph-In, Graph-Out, GIGO）-ToM，它能在任意计算机网络拓扑上准确预测敌对网络智能体的目标和攻击轨迹。为评估后者，我们提出了一种基于Wasserstein距离的扩展方法，用于测量基于图的概率分布的相似性。由于标准Wasserstein距离缺乏固定参考尺度，我们引入了一种图论归一化因子，使不同规模网络之间的标准化比较成为可能。我们为这一被称为网络传输距离（Network Transport Distance, NTD）的指标配备了加权函数，该函数根据自定义节点特征强调预测，允许网络运营商探索任意战略考量。在抽象网络防御环境中，与图入密集出（Graph-In, Dense-Out, GIDO）-ToM架构进行基准测试的实证评估表明，GIGO-ToM能在多种网络拓扑上准确预测各种未见过的网络攻击智能体的目标和行为，并能学习有效表征其策略的嵌入向量。
英文摘要：
Intelligent autonomous agents hold much potential for the domain of cyber-security. However, due to many state-of-the-art approaches relying on uninterpretable black-box models, there is growing demand for methods that offer stakeholders clear and actionable insights into their latent beliefs and motivations. To address this, we evaluate Theory of Mind (ToM) approaches for Autonomous Cyber Operations. Upon learning a robust prior, ToM models can predict an agent's goals, behaviours, and contextual beliefs given only a handful of past behaviour observations. In this paper, we introduce a novel Graph Neural Network (GNN)-based ToM architecture tailored for cyber-defence, Graph-In, Graph-Out (GIGO)-ToM, which can accurately predict both the targets and attack trajectories of adversarial cyber agents over arbitrary computer network topologies. To evaluate the latter, we propose a novel extension of the Wasserstein distance for measuring the similarity of graph-based probability distributions. Whereas the standard Wasserstein distance lacks a fixed reference scale, we introduce a graph-theoretic normalization factor that enables a standardized comparison between networks of different sizes. We furnish this metric, which we term the Network Transport Distance (NTD), with a weighting function that emphasizes predictions according to custom node features, allowing network operators to explore arbitrary strategic considerations. Benchmarked against a Graph-In, Dense-Out (GIDO)-ToM architecture in an abstract cyber-defence environment, our empirical evaluations show that GIGO-ToM can accurately predict the goals and behaviours of various unseen cyber-attacking agents across a range of network topologies, as well as learn embeddings that can effectively characterize their policies.
Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
Electrical and Computer Engineering Department, Royal Military College of Canada（加拿大皇家军事学院电子与计算机工程系）；Defence Research and Development Canada（加拿大国防研究发展署）
本文提出在Cyber Operations Research Gym中利用可微分智能体间学习算法训练防御智能体，使其在部分可观测网络环境中学习低成本通信与防御战术策略，从而提升自主网络防御的协同决策能力。
Learning Communication Between Heterogeneous Agents in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
Department of Electrical and Computer Engineering, Royal Military College of Canada（加拿大皇家军事学院电子与计算机工程系）；Defence Research and Development Canada（加拿大国防研究与发展加拿大）；Department of Computer Engineering and Software Engineering, Polytechnique Montréal（蒙特利尔理工学院计算机工程与软件工程系）
Autonomous Cyber Operations Gym for Training and Evaluating AI Models for Defence
CortAIx Labs, Thales Deutschland（Thales Deutschland CortAIx 实验室）；University of Osnabrück（奥斯纳布吕克大学）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；University of Ulm（乌尔姆大学）
