Adversarial attacks on intrusion detection systems in in-vehicle networks of connected and autonomous vehicles

Aloraini, Fatimah ORCID: https://orcid.org/0000-0001-5494-0661, Javed, Amir ORCID: https://orcid.org/0000-0001-9761-0945 and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2024. Adversarial attacks on intrusion detection systems in in-vehicle networks of connected and autonomous vehicles. Sensors 24 (12) , 3848. 10.3390/s24123848

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (885kB) | Preview

Abstract

Rapid advancements in connected and autonomous vehicles (CAVs) are fueled by breakthroughs in machine learning, yet they encounter significant risks from adversarial attacks. This study explores the vulnerabilities of machine learning-based intrusion detection systems (IDSs) within in-vehicle networks (IVNs) to adversarial attacks, shifting focus from the common research on manipulating CAV perception models. Considering the relatively simple nature of IVN data, we assess the susceptibility of IVN-based IDSs to manipulation—a crucial examination, as adversarial attacks typically exploit complexity. We propose an adversarial attack method using a substitute IDS trained with data from the onboard diagnostic port. In conducting these attacks under black-box conditions while adhering to realistic IVN traffic constraints, our method seeks to deceive the IDS into misclassifying both normal-to-malicious and malicious-to-normal cases. Evaluations on two IDS models—a baseline IDS and a state-of-the-art model, MTH-IDS—demonstrated substantial vulnerability, decreasing the F1 scores from 95% to 38% and from 97% to 79%, respectively. Notably, inducing false alarms proved particularly effective as an adversarial strategy, undermining user trust in the defense mechanism. Despite the simplicity of IVN-based IDSs, our findings reveal critical vulnerabilities that could threaten vehicle safety and necessitate careful consideration in the development of IVN-based IDSs and in formulating responses to the IDSs’ alarms.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	MDPI
ISSN:	1424-8220
Date of First Compliant Deposit:	14 June 2024
Date of Acceptance:	12 June 2024
Last Modified:	27 Jun 2024 13:45
URI:	https://orca.cardiff.ac.uk/id/eprint/169837

Actions (repository staff only)

Edit Item