We believe that agents for automated incident response based on machine learning need to handle changes in network structure. Computer networks are dynamic, and can naturally change in structure over time. Retraining agents for small network changes costs time and energy. We attempt to address this issue with an existing method of relational agent learning, where the relations between objects are assumed to remain consistent across problem instances. The state of the computer network is represented as a relational graph and encoded through a message passing neural network. The message passing neural network and an agent policy using the encoding are optimized end-to-end using reinforcement learning. We evaluate the approach on the second instance of the Cyber Autonomy Gym for Experimentation (CAGE~2), a cyber incident simulator that simulates attacks on an enterprise network. We create variants of the original network with different numbers of hosts and agents are tested without additional training on them. Our results show that agents using relational information are able to find solutions despite changes to the network, and can perform optimally in some instances. Agents using the default vector state representation perform better, but need to be specially trained on each network variant, demonstrating a trade-off between specialization and generalization.

基于机器学习的自动化事件响应代理需要处理网络结构的变化，通过使用关系代理学习方法，将计算机网络的状态表示为关系图，并使用信息传递神经网络对其进行编码，从而解决了在网络变化时重新训练代理的问题。

基于消息传递神经网络和强化学习的自主网络安全事件响应中的结构泛化