CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation
This addresses the need for large datasets to develop Intrusion Detection Systems for automotive cybersecurity, but it is incremental as it builds on existing simulation frameworks.
The paper tackles the problem of generating synthetic datasets for CAN network attacks in vehicles by introducing CARACAS, a vehicular simulation model that integrates Simulink with attack injection capabilities, resulting in a demonstrated methodology for creating detailed attack scenarios.
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.