TY - GEN
T1 - The Threat Landscape of Connected Vehicles
AU - Dharshana Jayaratne, Don Nalin
AU - Lu, Qian
AU - Rakib, Abdur
AU - Ramli, Muhamad Azfar
AU - Mepparambath, Rakhi Manohar
AU - Shaikh, Siraj Ahmed
AU - Nguyen, Hoang Nga
AU - Kamtam, Suraj Harsha
N1 - This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.
PY - 2025
Y1 - 2025
N2 - As connected vehicles (CVs) play an increasingly pivotal role in modern transportation, cybersecurity threats targeting these systems have become a critical area of concern. This study systematically identifies and classifies vulnerabilities from the National Vulnerability Database (NVD) and the Automotive Attack Database (AAD) using a semi-automated filtering process. Our analysis identifies a total of 508 vulnerabilities across these databases, which are categorised based on ISO/SAE 21434 impact categories: safety, financial, operational, and privacy. A key finding reveals that 14.6\% of these vulnerabilities have systemic implications, meaning they have the potential to cause widespread disruption across multiple vehicles or the broader transportation network. Furthermore, 45\% of the vulnerabilities are associated with remote attack vectors, significantly increasing the risk of large-scale exploitation. This research contributes an updated database of automotive vulnerabilities, providing a valuable resource for the cybersecurity community. The findings highlight the need to enhance current automotive cybersecurity standards, such as ISO/SAE 21434, to address the complex inter-dependencies and systemic risks within connected vehicle ecosystems.
AB - As connected vehicles (CVs) play an increasingly pivotal role in modern transportation, cybersecurity threats targeting these systems have become a critical area of concern. This study systematically identifies and classifies vulnerabilities from the National Vulnerability Database (NVD) and the Automotive Attack Database (AAD) using a semi-automated filtering process. Our analysis identifies a total of 508 vulnerabilities across these databases, which are categorised based on ISO/SAE 21434 impact categories: safety, financial, operational, and privacy. A key finding reveals that 14.6\% of these vulnerabilities have systemic implications, meaning they have the potential to cause widespread disruption across multiple vehicles or the broader transportation network. Furthermore, 45\% of the vulnerabilities are associated with remote attack vectors, significantly increasing the risk of large-scale exploitation. This research contributes an updated database of automotive vulnerabilities, providing a valuable resource for the cybersecurity community. The findings highlight the need to enhance current automotive cybersecurity standards, such as ISO/SAE 21434, to address the complex inter-dependencies and systemic risks within connected vehicle ecosystems.
KW - Automotive cybersecurity
KW - Connected vehicles
KW - National Vulnerability Database
KW - Vulnerability classification
KW - Vulnerability impact assessment
UR - https://www.scopus.com/pages/publications/105007090285
U2 - 10.1007/978-3-031-82031-1_13
DO - 10.1007/978-3-031-82031-1_13
M3 - Conference proceeding
SN - 978-3-031-82030-4
T3 - Advanced Sciences and Technologies for Security Applications
SP - 227
EP - 247
BT - Cybersecurity and Human Capabilities Through Symbiotic Artificial Intelligence
A2 - Jahankhani, Hamid
A2 - Issac, Biju
PB - Springer
T2 - 16th International Conference on Global Security, Safety & Sustainability
Y2 - 25 November 2024 through 27 November 2024
ER -
