Modern vehicles are increasingly vulnerable to attacks that exploit network
infrastructures, particularly the Controller Area Network (CAN) networks. To
effectively counter such threats using contemporary tools like Intrusion
Detection Systems (IDSs) based on data analysis and classification, large
datasets of CAN messages become imperative. This paper delves into the
feasibility of generating synthetic datasets by harnessing the modeling
capabilities of simulation frameworks such as Simulink coupled with a robust
representation of attack models to present CARACAS, a vehicular model,
including component control via CAN messages and attack injection capabilities.
CARACAS showcases the efficacy of this methodology, including a Battery
Electric Vehicle (BEV) model, and focuses on attacks targeting torque control
in two distinct scenarios.

现代车辆对利用网络基础设施的攻击（尤其是 Controller Area Network 网络）越来越容易受到威胁。本文通过利用 Simulink 等仿真框架的建模能力以及攻击模型的强大表示，探讨了通过生成合成数据集来有效应对此类威胁的可行性，并呈现了 CARACAS，一种包括通过 Controller Area Network 消息进行组件控制和攻击注入功能的车辆模型。CARACAS 展示了这种方法的功效，包括一个电池电动汽车模型，并侧重于针对扭矩控制的两种不同情景中的攻击。