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.

翻译：现代车辆日益容易受到利用网络基础设施（尤其是控制器局域网（CAN）网络）的攻击。为了有效应对此类威胁，并采用基于数据分析和分类的入侵检测系统（IDS）等现代工具，获取大规模的CAN消息数据集变得至关重要。本文深入探讨了利用Simulink等仿真框架的建模能力，结合攻击模型的鲁棒表示来生成合成数据集的可行性，并提出了CARACAS——一种包含通过CAN消息进行组件控制及攻击注入功能的车辆模型。CARACAS展示了该方法的有效性，其包含一个纯电动汽车（BEV）模型，并重点研究了两种不同场景下针对扭矩控制的攻击。