.png){kind=icon}
Return
Reinforcement Learning Based PHY Authentication for VANETs
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2020.2967026.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Mobile edge computing in vehicular ad hoc networks (VANETs) suffers from rogue edge attacks due to the vehicle mobility and the network scale. In this paper, we present a physical authentication scheme to resist rogue edge attackers whose goal is to send spoofing signals to attack VANETs. This authentication scheme exploits the channel states of the shared ambient radio signals of the mobile device and its serving edge such as the onboard unit during the same moving trace and applies reinforcement learning to select the authentication modes and parameters. By applying transfer learning to save the learning time and applies deep learning to further improve the authentication performance, this scheme enables mobile devices in VANETs to optimize their authentication modes and parameters without being aware of the VANET channel model, the packet generation model, and the spoofing model. We provide the convergence bound such as the mobile device utility, evaluate the computational complexity of the physical authentication scheme, and verify the analysis results via simulations. Simulation and experimental results show that this scheme improves the authentication accuracy with reduced energy consumption against rogue edge attacks.
Keywords:
VANETs
rogue edge attacks
physical authentication
ambient radio signals
reinforcement learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
Organization
Cited Papers
On the Error Region for Channel Estimation-Based Physical Layer Authentication Over Rayleigh Fading
OAAI
.png){kind=icon}
OAAIShareSaveShareSaveShareSaveShareSaveShareSave
View more