| Title | CAN-FT: A Fuzz Testing Method for Automotive Controller Area Network Bus |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Haichun, Huang, Kelin, Wang, Jie, Liu, Zhenglin |
| Conference Name | 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI) |
| Keywords | AdaBoost, automobiles, boosting, CAN bus, controller area network security, Cyber-physical systems, Explosions, Fuzz Testing, fuzzing, generative adversarial networks, information science, Internet of Things, pubcrawl, Resiliency, security |
| Abstract | The Controller Area Network (CAN) bus is the de-facto standard for connecting the Electronic Control Units (ECUs) in automobiles. However, there are serious cyber-security risks due to the lack of security mechanisms. In order to mine the vulnerabilities in CAN bus, this paper proposes CAN-FT, a fuzz testing method for automotive CAN bus, which uses a Generative Adversarial Network (GAN) based fuzzy message generation algorithm and the Adaptive Boosting (AdaBoost) based anomaly detection mechanism to capture the abnormal states of CAN bus. Experimental results on a real-world vehicle show that CAN-FT can find vulnerabilities more efficiently and comprehensively. |
| DOI | 10.1109/CISAI54367.2021.00050 |
| Citation Key | zhang_can-ft_2021 |
CAN-FT: A Fuzz Testing Method for Automotive Controller Area Network Bus