为6G车联网设计自适应量子安全加密,动态优化性能与安全平衡。
Adaptive Quantum-Safe Cryptography for 6G Vehicular Networks via Context-Aware Optimization
- 基于预测的多目标进化算法动态选择合适密码配置。
- 实测降低27%端到端延迟,通信开销减少65%。
- 适合对安全性与实时性要求高的智能网联汽车场景。
未来强大的量子计算机可能破解车辆与设备间通信(车对外,V2X)的安全机制。后量子密码学(PQC)可提供保护,但通常需要更高算力,影响6G车联网的响应速度。本文提出一种自适应后量子加密框架,通过预测短期移动性与信道变化,利用预测型多目标进化算法(APMOEA)动态选择基于格、码或哈希的PQC配置,以满足车载低时延与安全需求。然而,动态加密配置切换可能引入新攻击面。为此,设计了安全单调升级协议,防范降级、重放与不同步攻击。理论分析表明,在有限预测误差下决策稳定,移动漂移下时延有界,小预报噪声下正确性成立。基于真实移动性(LuST)、天气(ERA5)及NR-V2X信道数据的大量实验显示,该框架将端到端延迟降低最高达27%,通信开销减少最高达65%,并借助强化学习有效稳定加密切换行为。在评估的对抗场景中,单调升级协议成功阻止了降级、重放和不同步攻击。
原文摘要 · Abstract (English)
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.
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