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Connected and autonomous vehicles (CAVs) rely on communication channels to improve safety and efficiency. However, this connectivity leaves them vulnerable to potential cyberattacks, such as false data injection (FDI) attacks. We can mitigate the effect of FDI attacks by designing secure control techniques. However, tuning control parameters is essential for the safety and security of such techniques, and there is no systematic approach to achieving that. In this article, our primary focus is on cooperative adaptive cruise control (CACC), a key component of CAVs. We develop a secure CACC by integrating model-based and learning-based approaches to detect and mitigate FDI attacks in real-time. We analyze the stability of the proposed resilient controller through Lyapunov stability analysis, identifying sufficient conditions for its effectiveness. We use these sufficient conditions and develop a reinforcement learning (RL)-based tuning algorithm to adjust the parameter gains of the controller, observer, and FDI attack estimator, ensuring the safety and security of the developed CACC under varying conditions. We evaluated the performance of the developed controller before and after optimizing parameters, and the results show about a 50% improvement in accuracy of the FDI attack estimation and a 76% enhancement in safe following distance with the optimized controller in each scenario.
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Cybersecurity Vulnerabilities for Off-Board Commercial Vehicle Diagnostics
The lack of inherent security controls makes traditional Controller Area Network (CAN) buses vulnerable to Machine-In-The-Middle (MitM) cybersecurity attacks. Conventional vehicular MitM attacks involve tampering with the hardware to directly manipulate CAN bus traffic. We show, however, that MitM attacks can be realized without direct tampering of any CAN hardware. Our demonstration leverages how diagnostic applications based on RP1210 are vulnerable to Machine-In-The-Middle attacks. Test results show SAE J1939 communications, including single frame and multi-framed broadcast and on-request messages, are susceptible to data manipulation attacks where a shim DLL is used as a Machine-In-The-Middle. The demonstration shows these attacks can manipulate data that may mislead vehicle operators into taking the wrong actions. A solution is proposed to mitigate these attacks by utilizing machine authentication codes or authenticated encryption with pre-shared keys between the communicating parties. Various tradeoffs, such as communication overhead encryption time and J1939 protocol compliance, are presented while implementing the mitigation strategy. One of our key findings is that the data flowing through RP1210-based diagnostic systems are vulnerable to MitM attacks launched from the host diagnostics computer. Security models should include controls to detect and mitigate these data flows. An example of a cryptographic security control to mitigate the risk of an MitM attack was implemented and demonstrated by using the SAE J1939 DM18 message. This approach, however, utilizes over twice the bandwidth as normal communications. Sensitive data should utilize such a security control.
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- Award ID(s):
- 2018912
- PAR ID:
- 10496473
- Publisher / Repository:
- SAE.org
- Date Published:
- Journal Name:
- SAE International Journal of Advances and Current Practices in Mobility
- Volume:
- 5
- Issue:
- 6
- ISSN:
- 2641-9645
- Page Range / eLocation ID:
- 2393 to 2404
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.4271/2023-01-0040
