Evaluating Vulnerabilities of Connected Vehicles Under Cyber Attacks by Attack-Defense Tree

TL;DR

This paper addresses cybersecurity vulnerabilities in connected and autonomous vehicles (CAVs) by leveraging attack-defense trees to model attacker goals, attack steps, and defenses. It introduces a quantitative vulnerability metric $ν(\mathcal{I}_i)$ and an associated mathematical framework to evaluate how defenses reduce risk across attack leaves. The authors apply the model to a CAV ecosystem with RSUs, CAN buses, and V2X, demonstrating how existing defenses compare to improved countermeasures and highlighting key attack paths. The work provides a structured, data-driven approach for prioritizing security investments and motivates future work in dynamic dependencies and probabilistic risk assessment for resilient CAV architectures.

Abstract

Connected vehicles represent a key enabler of intelligent transportation systems, where vehicles are equipped with advanced communication, sensing, and computing technologies to interact not only with one another but also with surrounding infrastructures and the environment. Through continuous data exchange, such vehicles are capable of enhancing road safety, improving traffic efficiency, and ensuring more reliable mobility services. Further, when these capabilities are integrated with advanced automation technologies, the concept essentially evolves into connected and autonomous vehicles (CAVs). While connected vehicles primarily focus on seamless information sharing, autonomous vehicles are mainly dependent on advanced perception, decision-making, and control mechanisms to operate with minimal or without human intervention. However, as a result of connectivity, an adversary with malicious intentions might be able to compromise successfully by breaching the system components of CAVs. In this paper, we present an attack-tree based methodology for evaluating cyber security vulnerabilities in CAVs. In particular, we utilize the attack-defense tree formulation to systematically assess attack-leaf vulnerabilities, and before analyzing the vulnerability indices, we also define a measure of vulnerabilities, which is based on existing cyber security threats and corresponding defensive countermeasures.

Figures (4)

• Figure 1: System model components within the CAV ecosystem.
• Figure 2: The developed attack defense for CAVs with example attacks and defenses.
• Figure 3: The attack leaves are structured according to "AND" and "OR" logical operators.
• Figure 4: Vulnerability evaluating of connected vehicles in quantitative manner.