Adaptive Quantum-Safe Cryptography for 6G Vehicular Networks via Context-Aware Optimization

TL;DR

This paper introduces an adaptive, secure, and efficient quantum-safe cryptography framework for 6G vehicular networks that dynamically optimizes cryptographic configurations based on mobility and channel predictions.

Contribution

It proposes a novel adaptive PQC framework with a secure transition protocol, integrating predictive algorithms and reinforcement learning for enhanced security and performance in vehicular environments.

Findings

Reduces end-to-end latency by up to 27%

Lowers communication overhead by up to 65%

Prevents downgrade, replay, and desynchronization attacks during cryptographic transitions

Abstract

Powerful quantum computers in the future may be able to break the security used for communication between vehicles and other devices (Vehicle-to-Everything, or V2X). New security methods called post-quantum cryptography can help protect these systems, but they often require more computing power and can slow down communication, posing a challenge for fast 6G vehicle networks. In this paper, we propose an adaptive post-quantum cryptography (PQC) framework that predicts short-term mobility and channel variations and dynamically selects suitable lattice-, code-, or hash-based PQC configurations using a predictive multi-objective evolutionary algorithm (APMOEA) to meet vehicular latency and security constraints.However, frequent cryptographic reconfiguration in dynamic vehicular environments introduces new attack surfaces during algorithm transitions. A secure monotonic-upgrade protocol prevents downgrade, replay, and desynchronization attacks during transitions. Theoretical results show decision stability under bounded prediction error, latency boundedness under mobility drift, and correctness under small forecast noise. These results demonstrate a practical path toward quantum-safe cryptography in future 6G vehicular networks. Through extensive experiments based on realistic mobility (LuST), weather (ERA5), and NR-V2X channel traces, we show that the proposed framework reduces end-to-end latency by up to 27\%, lowers communication overhead by up to 65\%, and effectively stabilizes cryptographic switching behavior using reinforcement learning. Moreover, under the evaluated adversarial scenarios, the monotonic-upgrade protocol successfully prevents downgrade, replay, and desynchronization attacks.