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

2026-02-01

AI Summary

This work addresses the dual challenge of ultra-low latency and quantum-resistant security in 6G vehicular networks, where existing post-quantum cryptography (PQC) schemes incur high overhead and lack dynamic adaptability. To bridge this gap, the authors propose an adaptive PQC framework that uniquely integrates context awareness—encompassing vehicle mobility, channel conditions, and weather—with cryptographic protocol design. Leveraging a predictive multi-objective evolutionary algorithm (APMOEA), the framework dynamically selects optimal PQC primitives from lattice-based, code-based, or hash-based families. A secure monotonic upgrade mechanism ensures robustness during cryptographic transitions. Evaluated in realistic NR-V2X scenarios, the approach reduces end-to-end latency by 27% and communication overhead by 65%, while significantly enhancing handover stability and effectively mitigating downgrade, replay, and desynchronization attacks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Applications of cryptography
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• post-quantum cryptography
• 6G vehicular networks
• V2X security
• quantum-safe
• cryptographic reconfiguration

Innovation

• adaptive post-quantum cryptography
• predictive multi-objective evolutionary algorithm
• monotonic-upgrade protocol
• 6G vehicular networks
• context-aware optimization