Detecting Stealthy Cyberattacks on Automated Vehicles via Generative Adversarial Networks

Tianyi Li, Mingfeng Shang, Shian Wang, Matthew Filippelli, Raphael Stern

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

The emergence of vehicles with driver-assist features, including adaptive cruise control (ACC) or other automated driving capabilities, introduces the possibility of cyberattacks where a select number of automated vehicles (AVs) are compromised to drive with adversarial controls. While obvious attacks that force vehicles to crash may be easily detectable, more subtle attacks are harder to detect and could change vehicle driving behavior, resulting in a network-wide increase in congestion and fuel consumption. To address this pressing problem, we first characterize two scenarios of potential cyberattacks, namely malicious attack on individual vehicles and data injection attack on sensor measurements. Then, a generative adversarial network (GAN)-based anomaly detection model is proposed for real-time detection of such attacks. Finally, to demonstrate the effectiveness of the proposed method we conduct numerical experiments using both synthetic vehicle trajectory data, and real-world ACC trajectory data with synthetic sensor attacks injected. The results show that the proposed model is capable of detecting attacks using a short period of trajectory data.

Original languageEnglish (US)
Title of host publication2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3632-3637
Number of pages6
ISBN (Electronic)9781665468800
DOIs
StatePublished - 2022
Event25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, China
Duration: Oct 8 2022Oct 12 2022

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2022-October

Conference

Conference25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Country/TerritoryChina
CityMacau
Period10/8/2210/12/22

Bibliographical note

Funding Information:
This work is supported by the University of Minnesota Center for Transportation Studies through the Transportation Scholar’s Program.

Publisher Copyright:
© 2022 IEEE.

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