A Model-Driven Methodology for Automotive Cybersecurity Test Case Generation

TL;DR

This paper introduces a model-driven, attack tree-based methodology to automate automotive cybersecurity testing, aiming to meet increasing regulatory demands and improve testing efficiency across the vehicle lifecycle.

Contribution

It proposes a novel semi-automated approach for black box cybersecurity testing in automotive systems using attack trees and model derivation techniques.

Findings

Enables comprehensive cybersecurity testing at all vehicle lifecycle stages

Facilitates third-party and real-world condition testing

Improves testing efficiency over manual methods

Abstract

Through international regulations (most prominently the latest UNECE regulation) and standards, the already widely perceived higher need for cybersecurity in automotive systems has been recognized and will mandate higher efforts for cybersecurity engineering. T he UNECE also demands the effectiveness of these engineering to be verified and validated through testing. T his requires both a significantly higher rate and more comprehensiveness of cybersecurity testing that is not effectively to cope with using current, predominantly manual, automotive cybersecurity testing techniques. To allow for comprehensive and efficient testing at all stages of the automotive life cycle, including supply chain parts not at band, and to facilitate efficient third party testing, as well as to test under real-world conditions, also methodologies for testing the cybersecurity of vehicular systems as a black box are necessary. T his paper therefore presents a model and attack tree-based approach to (semi-)automate automotive cybersecurity testing, as well as considerations for automatically black box-deriving models for the use in attack modeling.