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 language||English (US)|
|Title of host publication||2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - 2022|
|Event||25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, China|
Duration: Oct 8 2022 → Oct 12 2022
|Name||IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC|
|Conference||25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022|
|Period||10/8/22 → 10/12/22|
Bibliographical noteFunding Information:
This work is supported by the University of Minnesota Center for Transportation Studies through the Transportation Scholar’s Program.
© 2022 IEEE.