Dynamic encryption based secure key management for Vehicle-to-Vehicle communication in autonomous driving.

Nsour, A; Ganesan, S · 2026 · PubMed Central

DOI: 10.1038/s41598-026-50088-y

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Summary

This paper addresses the critical security challenges in Vehicle-to-Vehicle (V2V) communication for autonomous driving, specifically focusing on secure key management in highly dynamic network environments. Traditional encryption methods and static key management schemes are ill-suited for V2V networks due to rapid topology changes, the need for real-time processing, and limited computational resources in onboard units. Existing solutions, such as pseudonym-based authentication or blockchain-based systems, suffer from scalability bottlenecks, single points of failure, or excessive latency. To resolve these issues, the authors propose DASKM (Dynamic Autonomous Secure Key Management), a novel framework that integrates adaptive key lifecycle management with lightweight, context-aware encryption. The methodology employs a hierarchical and decentralized key distribution algorithm that adapts to real-time vehicular parameters, including speed, proximity, and network density. DASKM utilizes AES-128 in Counter (CTR) mode to minimize computational overhead, avoiding block-padding issues and leveraging hardware acceleration. Key rotation is event-driven rather than continuous, triggered only when specific thresholds for vehicle speed or proximity are crossed. Additionally, the system uses pre-computed group keys for vehicle platoons to reduce per-vehicle negotiation overhead. The authors formulate mathematical models to optimize latency and resource utilization, ensuring that encryption operations remain within strict time bounds (10–25 ms per packet) while maintaining NIST-recommended entropy levels. The system was evaluated through wide-ranging simulations across various traffic densities and communication standards, including DSRC, C-V2X, and IEEE 802.11p. The results demonstrate that DASKM significantly outperforms fixed encryption models. The proposed protocol reduced latency by 30% and improved security response times by 25%. In terms of threat resilience, DASKM achieved 95–96% resistance against man-in-the-middle, replay, and spoofing attacks, compared to lower resilience rates in prior studies. The system maintained stable throughput and low latency even in high-density fleet scenarios, scaling effectively from 50 to 300 concurrent vehicles. Computational overhead remained within the 10–25 ms budget required for standard onboard units, confirming the protocol’s lightweight nature. The significance of this work lies in providing a practically scalable solution for securing V2V communications in autonomous vehicle networks. By eliminating centralized dependencies and adapting encryption strength to real-time context, DASKM ensures data integrity and confidentiality without compromising the real-time performance essential for safety-critical applications. This approach offers a robust foundation for the deployment of safer, more trustworthy autonomous driving ecosystems, addressing the limitations of both static encryption and computationally intensive decentralized alternatives.

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• v2x connected vehicle