Open Access

ARTICLE

CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

Hyeon Park¹, SeoYeon Kim², Seok Min Ko¹, TaeGuen Kim^2,*

1 Department of Smart Convergence Security, Soonchunhyang University, Asan, Korea
2 Department of Information Security Engineering, Soonchunhyang University, Asan, Korea

* Corresponding Author: TaeGuen Kim. Email:

(This article belongs to the Special Issue: Advances in Information Security Application)

Computers, Materials & Continua 2023, 76(2), 1891-1909. https://doi.org/10.32604/cmc.2023.039464

Received 31 January 2023; Accepted 26 May 2023; Issue published 30 August 2023

Abstract

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.

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Keywords

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