TY  - EJOU
AU  - Park, Hyeon 
AU  - Kim, SeoYeon 
AU  - Ko, Seok Min 
AU  - Kim, TaeGuen 

TI  - CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 76
IS  - 2
SN  - 1546-2226

AB  - The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety. One key system that needs protection is the passive key entry system (PKES). To prevent attacks aimed at defeating the PKES, we propose a novel radio frequency (RF) fingerprinting method. Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal. This feature is then analyzed using a convolutional neural network (CNN) for device identification. In evaluation, we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model. Our experimental results revealed that the Gammatone Frequency Cepstral Coefficient (GFCC) was the most compelling feature compared to Mel-Frequency Cepstral Coefficient (MFCC), Inverse Mel-Frequency Cepstral Coefficient (IMFCC), Linear-Frequency Cepstral Coefficient (LFCC), and Bark-Frequency Cepstral Coefficient (BFCC). Additionally, we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.
KW  - RF fingerprint; cepstral coefficient; convolutional neural network

DO  - 10.32604/cmc.2023.039464