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

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.

💡 Research Summary

The paper addresses the looming threat that large‑scale quantum computers pose to current Vehicle‑to‑Everything (V2X) security, which largely relies on elliptic‑curve and RSA primitives. Deploying post‑quantum cryptography (PQC) in 6G vehicular networks is challenging because PQC schemes (lattice‑based, code‑based, hash‑based) typically require larger keys, higher computational effort, and greater communication overhead, potentially violating the ultra‑reliable low‑latency communication (URLLC) requirements of safety‑critical vehicular applications.
To overcome this, the authors propose a Context‑Aware Adaptive PQC (CAAP) framework that dynamically selects the most suitable PQC configuration based on short‑term predictions of vehicle mobility, channel conditions, weather, and on‑board computational load. The framework consists of four components:

1. Context‑Sensing Pipeline – collects a unified context vector every 20–50 ms, including speed, connection duration, SNR, packet error rate, weather visibility, CPU/GPU load, and message urgency.
2. Short‑Term Predictor – uses lightweight filters and regression models to forecast the context for the next 100–200 ms, providing a stable basis for decision making while keeping prediction overhead low.
3. Adaptive Predictive Multi‑Objective Evolutionary Algorithm (APMOEA) – receives the predicted context and a cost vector for each candidate PQC algorithm (encryption/decryption time, key size, ciphertext size, computational energy, signature size). APMOEA simultaneously optimizes four objectives—latency, computational cost, communication overhead, and quantum‑resilience—by evolving a population of configurations. Reinforcement‑learning feedback continuously steers the search toward regions that historically yielded better performance.
4. Secure Transition Protocol – enforces monotonic, authenticated upgrades of PQC versions. By exchanging signed version counters and version‑nonce messages, the protocol guarantees that a node can never downgrade to a weaker scheme, and it prevents replay and desynchronization attacks. The design mirrors the downgrade‑protection mechanisms of TLS 1.3 and QUIC but is streamlined for the sub‑100 ms round‑trip times typical of V2X.

The authors provide theoretical guarantees: (i) decision stability when prediction error stays within a bounded range, (ii) an end‑to‑end latency bound under bounded mobility drift, and (iii) correctness of the selected configuration relative to an oracle selector when forecast noise is small. These proofs give confidence that the adaptive layer will not introduce instability in safety‑critical loops.

Experimental evaluation combines realistic datasets: the LuST traffic simulator for vehicle trajectories, ERA5 reanalysis for weather, 3GPP‑compliant NR‑V2X channel models, and automotive‑grade compute profiles. The CAAP framework is compared against (a) static PQC configurations, (b) a classic NSGA‑II multi‑objective optimizer, and (c) a reinforcement‑learning‑only selector. Results show up to 27 % reduction in end‑to‑end latency and 65 % reduction in communication overhead while meeting URLLC latency targets (5–20 ms). The monotonic‑upgrade protocol successfully thwarts all simulated downgrade, replay, and desynchronization attacks, confirming its robustness.

Key contributions include:

• An end‑to‑end adaptive planning pipeline that integrates real‑time context sensing, short‑term prediction, and multi‑objective optimization for PQC selection.
• A lightweight, authenticated transition protocol that guarantees monotonic upgrades and protects against downgrade‑type attacks in highly dynamic V2X environments.
• Formal analysis establishing stability and bounded latency under realistic vehicular dynamics.
• Extensive, realistic evaluation demonstrating significant performance gains and security resilience.

Strengths of the work are its holistic approach (combining sensing, prediction, optimization, and security), rigorous theoretical analysis, and thorough experimental validation with realistic traces. Potential limitations include reliance on relatively simple prediction models that may struggle with highly non‑linear dynamics, the computational cost of reinforcement‑learning updates on constrained automotive hardware, and the fact that the framework assumes a future 6G V2X stack that is not yet standardized. Future research directions suggested are: employing deep‑learning‑based predictors for higher accuracy, hardware acceleration of the RL component, extending the framework to support hybrid PQC configurations (simultaneous KEM and signature), and collaborating with standardization bodies to align the protocol with emerging 6G specifications.