Integrated Automation for Threat Analysis and Risk Assessment in Automotive Cybersecurity Through Attack Graphs

Abstract

Attack graphs contribute to the evaluation of network security vulnerabilities, offering a visualization of possible attack paths. Despite their common use in IT security for analyzing system vulnerabilities, attack graphs are not commonly used in the automotive sector. As smart vehicles increasingly rely on 5G networks for high-bandwidth, low-latency communication – necessary for advanced vehicle-to-everything (V2X) services and sensor data processing – security concerns escalate. The complexity of 5G-enabled vehicles significantly expands a vehicle's attack surface. The ISO/SAE 21434 standard establishes a framework for securing road vehicle systems. The Threat Analysis and Risk Assessment (TARA) process, a vital part of this standard, helps identify and mitigate security risks. However, the current TARA process relies heavily on manual effort to identify potential attack vectors and assess risks. This can be time consuming, resource- intensive, and prone to human error. This paper discusses the concept of an automated attack graph generation tool specifically designed for automotive threat analysis. We propose a new Graph-based Attack Path Prioritization tool (GAPP), tailored for automotive networks. GAPP focuses on generating attack paths, assessing their feasibility, and identifying the most likely attack scenarios. This aims to enhance the efficiency, comprehensiveness, and accuracy of the TARA process in evaluating network security.