CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation

TL;DR

This paper introduces CARACAS, a simulation framework for generating synthetic CAN message datasets, including attack scenarios, to aid in developing intrusion detection systems for vehicle networks.

Contribution

CARACAS is a novel simulation-based framework that models vehicle components and attack scenarios for creating detailed CAN message datasets.

Findings

CARACAS effectively simulates CAN messages and attack injections.

The framework includes a Battery Electric Vehicle model with attack scenarios.

Synthetic datasets generated by CARACAS support IDS development.

Abstract

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.