Secure and Private Sensing for Driver Authentication and Transportation Safety : Final Report

Voris, Jonathan; Artan, N. Sertac; Li, Wenjia · 2017 · ROSA P / City University of New York. University Transportation Research Center

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Summary

This report addresses the security and privacy vulnerabilities inherent in modern vehicular data collection systems, specifically focusing on driver authentication. While technological advancements have enabled efficient real-time data gathering for applications like usage-based insurance and traffic planning, these systems often rely on interfaces like On-Board Diagnostics (OBD-II) or GPS tracking that expose vehicles to cyber-attacks and privacy violations. Traditional token-based authentication, such as ignition keys or RFID tags, is insufficient for detecting vehicle misuse, cloning, or mid-session attacks like carjacking. The research aims to develop a secure, privacy-preserving method for continuously authenticating drivers by analyzing unique behavioral characteristics rather than relying on static tokens or critical vehicle components. To achieve this, the researchers conducted a study using a simulated driving environment to collect sensory data decoupled from the vehicle’s internal networks, thereby minimizing attack vectors and privacy risks. The study involved human subjects who performed driving tasks while sensors recorded specific operational habits, including steering wheel position and pedal pressure. This approach allows for the identification of distinctive underlying characteristics of individual driving behaviors. The collected data was analyzed using a Support Vector Machine (SVM) learning algorithm to classify drivers based on these behavioral biometrics. The experimental design prioritized a minimally invasive set of monitoring sensors to ensure that the data collected would not compromise vehicle security or driver privacy if exposed. The results demonstrate that driver identification is feasible using this behavioral sensing approach. The system successfully identified drivers in less than 2.5 minutes with a 95% confidence interval. Furthermore, the method exhibited high reliability, producing at most one false positive per driving day. Unlike offline or session-start authentication methods, this approach supports continuous authentication throughout a driving session, enabling the detection of mid-session anomalies such as unauthorized takeovers. The study confirms that various aspects of how a vehicle is operated can uniquely categorize individuals, providing a robust basis for active driver identification. The significance of this work lies in its ability to enhance transportation safety and security without sacrificing privacy or introducing new vulnerabilities to critical vehicular systems. By decoupling sensing from the vehicle’s control networks, the proposed solution mitigates risks associated with OBD-II attacks and remote exploits. This technology offers practical applications for various stakeholders, including municipal governments ensuring authorized bus operators, car-sharing services verifying member identity, and insurance providers preventing fraud. Ultimately, the research provides a viable framework for secure, continuous driver authentication that addresses the growing need for trust in intelligent transportation systems and smart city infrastructures.

Key finding

The system successfully identified drivers within 2.5 minutes with a 95% confidence interval and at most one false positive per driving day.

Methodology

simulator

Sample size: 10

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• telematics crash prediction
• alcohol detection systems
• exposure measurement

Information type

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• Methodological Resource: tool software, validation psychometrics, dataset resource