Poushali Sengupta (University of Oslo), Mayank Raikwar (University of Oslo), Sabita Maharjan (University of Oslo), Frank Eliassen (University of Oslo), Yan Zhang (University of Oslo)

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.

View More Papers

VeriLoRA: Fine-Tuning Large Language Models with Verifiable Security via...

Guofu Liao (Shenzhen University), Taotao Wang (Shenzhen University), Shengli Zhang (Shenzhen University), Jiqun Zhang (Shenzhen University), Long Shi (Nanjing University of Science and Technology), Dacheng Tao (Nanyang Technological University)

Read More

Light2Lie: Detecting Deepfake Images Using Physical Reflectance Laws

Kavita Kumari (Technical University of Darmstadt), Sasha Behrouzi (Technical University of Darmstadt), Alessandro Pegoraro (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

Read More

The Dark Side of Flexibility: Detecting Risky Permission Chaining...

Xunqi Liu (State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University), Nanzi Yang (University of Minnesota), Chang Li (State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University), Jinku Li (State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University), Jianfeng Ma (State Key Laboratory…

Read More