Open Access

ARTICLE

DriveMe: Towards Lightweight and Practical Driver Authentication System Using Single-Sensor Pressure Data

Mohsen Ali Alawami¹, Dahyun Jung², Yewon Park², Yoonseo Ku², Gyeonghwan Choi², Ki-Woong Park^2,*

1 Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin-si, 17035, Republic of Korea
2 Department of Information Security, Sejong University, Seoul, 05006, Republic of Korea

* Corresponding Author: Ki-Woong Park. Email:

(This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)

Computer Modeling in Engineering & Sciences 2025, 143(2), 2361-2389. https://doi.org/10.32604/cmes.2025.063819

Received 24 January 2025; Accepted 25 March 2025; Issue published 30 May 2025

Abstract

To date, many previous studies have been proposed for driver authentication; however, these solutions have many shortcomings and are still far from practical for real-world applications. In this paper, we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system, dubbed DriveMe, for identifying drivers on cars. Our novelty aspects are ① Lightweight scheme that depends only on a single sensor data (i.e., pressure readings) attached to the driver’s seat and belt. ② Practical evaluation in which one-class authentication models are trained from only the owner users and tested using data collected from both owners and attackers. ③ Rapid Authentication to quickly identify drivers’ identities using a few pressure samples collected within short durations (1, 2, 3, 5, or 10 s). ④ Realistic experiments where the sensory data is collected from real experiments rather than computer simulation tools. We conducted real experiments and collected about 13,200 samples and 22,800 samples of belt-only and seat-only datasets from all 12 users under different settings. To evaluate system effectiveness, we implemented extensive evaluation scenarios using four one-class detectors One-Class Support Vector Machine (OCSVM), Local Outlier Factor (LOF), Isolation Forest (IF), and Elliptic Envelope (EE), three dataset types (belt-only, seat-only, and fusion), and four different dataset sizes. Our average experimental results show that the system can authenticate the driver with an F1 score of 93.1% for seat-based data using OCSVM classifier, an F1 score of 98.53% for fusion-based data using LOF classifier, an F1 score of 91.65% for fusion-based data using IF classifier, and an F1 score of 95.79% for fusion-based data using EE classifier.

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