| Autor: |
Douglas G. Scruggs, Laxman Timilsina, Behnaz Papari, Ali Arsalan, Grace Muriithi, Gokhan Ozkan, Christopher S. Edrington |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 124220-124230 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3454560 |
| Popis: |
Autonomous electric vehicles provide benefits to both drivers and the environment compared to conventional vehicles; however, they are burdened with an increase in potential pathways for cyber-attacks. Therefore, reliable cyber-security strategies for these vehicles must be pursued. This paper addresses this concern by implementing a threat detection strategy that utilizes an observer and a neural network. These tools monitor discrepancies between the vehicle’s lateral metrics, which are produced via sensor data, neural network output, and an observer. Previous literature focuses on physics-based analytics to create the threat decision, but here, a data based approach is utilized. The vehicle used in this study is a four-motor-drive autonomous electric vehicle that is propelled with brushless DC motors. The motors are controlled by direct torque control. In this study, three forms of cyber-attacks are implemented. These include data integrity attacks, replay attacks, and denial-of-service attacks. A performance metric is also created, which indicates the data-driven approach outperforms the physics-based approaches. All modeling and simulation were conducted in the MATLAB/Simulink environment. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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