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.

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Ting Yang (Xidian University and Kanazawa University), Yue Qin (Central University of Finance and Economics), Lan Zhang (Northern Arizona University), Zhiyuan Fu (Hainan University), Junfan Chen (Hainan University), Jice Wang (Hainan University), Shangru Zhao (University of Chinese Academy of Sciences), Qi Li (Tsinghua University), Ruidong Li (Kanazawa University), He Wang (Xidian University), Yuqing Zhang (University…

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An LLM-Driven Fuzzing Framework for Detecting Logic Instruction Bugs...

Jiaxing Cheng (Institute of Information Engineering, CAS; SCS, UCAS Beijing, China), Ming Zhou (SCS, Nanjing University of Science and Technology Nanjing, Jiangsu, China), Haining Wang (ECE Virginia Tech Arlington, VA, USA), Xin Chen (Institute of Information Engineering, CAS; SCS, UCAS Beijing, China), Yuncheng Wang (Institute of Information Engineering CAS; SCS, UCAS Beijing, China), Yibo Qu…

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“NLIP: A Natural Language Approach to Securing IoT Devices”

Sanjay Aiyagari, Senior Principal Chief Architect, Red Hat

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