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

arXiv:2602.01342 · cs.CR, cs.AI, stat.AP · Submitted 2026-02-01 · Read on arXiv

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Introduction to the show: ident: Security Radio. Generated commentary on the latest security and cryptography papers.

Nadia: Today's paper: "Adaptive Quantum-Safe Cryptography for 6G Vehicular Networks via Context-Aware Optimization".

Elias: Powerful quantum computers may be able to break communication security for vehicles in 6G networks, necessitating new post-quantum cryptography methods that often introduce latency challenges.

Nadia: First, who's behind it and why it matters.

Paper summary: Nadia: Welcome back to the show. Today we’re looking at some heavy stuff in the 6G space, specifically how we can keep vehicle communications secure against future quantum computers using the paper titled "Adaptive Quantum-Safe Cryptography for 6G Vehicular Networks via Context-Aware Optimization <ref:2602.01342#pg0>." Elias, Nadia, what's the main takeaway from this paper regarding why this adaptive approach is necessary?

Elias: The core thesis of this paper is that powerful quantum computers might eventually be able to break the security used for communication between vehicles and other devices, which is a big problem since we move into 6G networks <ref:2602.01342#pg0>. So, new post-quantum cryptography methods are needed, but these often require more computing power and can slow down communication, creating a challenge for fast 6G vehicle networks <ref:2602.01342#pg0>. This paper proposes an adaptive post-quantum cryptography framework that predicts short-term mobility and channel variations to dynamically select the right PQC configurations—lattice-, code-, or hash-based—to meet the strict latency and security constraints of vehicles <ref:2602.01342#pg0>.

Priya: From a privacy and measurement standpoint, I’m interested in what these predictions actually look like in real-world data. Does this framework really account for how much noise or uncertainty there is in the context vector when we're looking at things like weather conditions or vehicle speed?

Nadia: That’s a fair question, Priya. The authors build a ContextSensing Pipeline to collect vehicle speed, communication quality, weather conditions, and message urgency into one unified context vector <ref:2602.01342#pg1>. They then use a Short-Term Predictor to anticipate these context changes over windows of one hundred to two hundred milliseconds using simple filters and regression models <ref:2602.01342#pg1>. Elias, does this predictive part give us enough stability for the cryptographic decisions they're making?

Elias: It’s designed to provide stable data for decision-making by anticipating context changes in those short windows using those filters and regression models <ref:2602.01342#pg1>. The APMOEA, which is the Adaptive Predictive Multi-Objective Evolutionary Algorithm, then uses reinforcement learning to continually adapt and reduce processing and communication delays while maximizing security <ref:2602.01342#pg1>. That learning aspect is crucial because it helps ensure effective PQC decisions across diverse vehicular environments <ref:2602.01342#pg1>.

Priya: So, when we look at the results, what kind of real-world data did they use to test this? I want to see if these models hold up against actual chaotic driving and channel conditions, not just idealized scenarios.

Nadia: They used extensive experiments with realistic traces that include LuST mobility traces, ERA5 weather data, and 3GPP-compliant NR-V2X channel models <ref:2602.01342#pg1>. These are not simple test cases; they simulate the actual complexity of vehicular environments <ref:2602.01342#pg1>. Elias, how does this real-world testing translate into the claimed improvements over existing methods?

Paper summary: Elias: The framework demonstrates a twenty-seven percent latency reduction and a communication overhead reduction of up to sixty-five percent when compared against static baselines <ref:2602.01342#pg1>. Furthermore, it shows full downgrade-attack resistance and improved robustness when compared to NSGA-II and RL-only approaches <ref:2602.01342#pg1>. The APMOEA uses a fitness function that combines end-to-end signing/verification latency, computational cost, communication overhead, and cryptographic strength <ref:2602.01342#pg1>.

Priya: I'm curious about the trade-offs here. If the system is optimizing for so many things—latency, computation cost, key size overhead—how does it ensure that maximizing one thing doesn't severely compromise another in a critical moment?

Nadia: That’s where the multi-objective nature of the APMOEA comes into play <ref:2602.01342#pg1>. The algorithm seeks Pareto optimal solutions under real-time constraints by balancing those factors using a cost vector that includes things like Tenc, Tdec, Skey, Sct, Ecomp, and Ssig <ref:2602.01342#pg1>. Elias, what about the stability aspect when the system is constantly switching between these configurations?

Elias: The authors provide formal guarantees to ensure stability through Theorem V.one of this paper, which establishes Decision Stability by proving that if Kε < ∆min, then the APMOEA selects the same algorithm at for all t within a context-stable interval <ref:2602.01342#pg1>. This means the system avoids unstable switching caused by noise or prediction errors <ref:2602.01342#pg1>. It also has Theorem V.three demonstrating Latency Boundedness, showing that PQC latency remains URLLC-compliant because context drift is bounded, ensuring Tlat(at, Xt) ≤ max a′∈A Tlat(a′, Xt) + O(δ) <ref:2602.01342#pg1>.

Priya: That stability claim is important for me because it suggests that the system won't just flip randomly based on minor fluctuations in the environment, which means more predictable privacy levels for the data being transmitted. What about security against malicious actors trying to exploit this adaptability?

Nadia: The threat model considers a powerful adversary who can inject, replay, delay, or modify packets <ref:2602.01342#pg2>. They might try to influence PQC selection by forging contextual features like SNR or PER <ref:2602.01342#pg2>, or they could try to exploit outdated version counters to trigger downgrade attacks <ref:2602.01342#pg1>. The framework is designed with a Secure Transition Protocol that enforces authenticated version-monotonic negotiation, which prevents downgrade and replay attacks during reconfiguration <ref:2602.01342#pg1>.

Elias: That monotonic PQC transition protocol is key to maintaining security integrity during these context changes <ref:2602.01342#pg1>. It's not just about choosing a configuration; it’s about how you transition between them securely, which the authors have designed as a lightweight, authenticated mechanism <ref:2602.01342#pg1>. The entire framework is focused on adaptive orchestration of standardized PQC mechanisms rather than introducing entirely new cryptographic primitives <ref:2602.01342#pg2>.

Paper summary: Priya: So, to wrap up this part, when we look at the broader implications, what does this adaptive orchestration mean for the real-world deployment of quantum-safe comms in vehicles? Where does this fit into the current landscape of V2X security?

Nadia: This work is about applying post-quantum cryptography at the application layer to protect V2V data communications transmitted over existing 5G connections, which is distinct from network-layer security like EAP-TLS <ref:2602.01342#pg0>. The implication is that we can move toward a system where PQC protection isn't a fixed, one-size-fits-all solution but something that intelligently adjusts to the immediate environment of the car <ref:2602.01342#pg1>.

Elias: From a cryptographic viewpoint, it’s about managing standardized mechanisms like Kyber or Dilithium in response to changing contexts <ref:2602.01342#pg2>. The idea is that by having the APMOEA continually adapt, we can manage the computational burden of these complex primitives effectively while maintaining strong security guarantees <ref:2602.01342#pg1>.

Priya: I think the most tangible impact for us as privacy researchers is seeing how this framework handles data integrity and confidentiality under dynamic conditions, ensuring that even when we switch cryptographic modes frequently, the overall data flow remains protected against known attack vectors <ref:2602.01342#pg1>.

Nadia: Exactly. The authors claim they've achieved a four point three switches per minute with reinforcement learning, which is a significant level of adaptation for such a sensitive process <ref:2602.01342#pg1>. This adaptive planning for PQC selection under vehicular constraints is what sets this work apart from static approaches <ref:2602.01342#pg1>.

Elias: It's fascinating how they’ve managed to prove decision stability under bounded prediction error, which is a strong theoretical result for a system that has to be constantly making real-time cryptographic choices <ref:2602.01342#pg1>. The formal guarantees are quite rigorous when you consider the adversarial objectives mentioned in the threat model <ref:2602.01342#pg2>.

Priya: So, looking ahead, what are the limitations they explicitly state? Where does this adaptive framework stop working or where might it struggle in a future scenario?

Nadia: The paper states that frequent cryptographic reconfiguration in dynamic vehicular environments introduces new attack surfaces during a transition period <ref:2602.01342#pg0>. So, while the system is robust against downgrade attacks and replay attacks via the secure transition protocol, there’s still a window of vulnerability during those actual switching moments <ref:2602.01342#pg0>.

Paper summary: Elias: And they also noted that this work does not introduce new post-quantum cryptographic primitives themselves; it focuses on the adaptive orchestration of standardized PQC mechanisms like Kyber or Dilithium <ref:2602.01342#pg2>. That’s a limitation in terms of introducing novel mathematical security tools.

Priya: It sounds like the authors are acknowledging that while their dynamic selection logic is strong, the underlying primitives themselves still have inherent security assumptions that need to be robust against this high-frequency switching <ref:2602.01342#pg1>. I think we need to keep an eye on how these standardized PQC mechanisms perform when subjected to this level of context drift <ref:2602.01342#pg1>.

Nadia: That’s a fair point, Priya. The performance gains they report—reducing latency by up to twenty-seven percent and overhead by up to sixty-five percent—are significant when you factor in the real-world complexity they modeled <ref:2602.01342#pg1>. It shows that intelligent orchestration can make a big difference in practical deployment scenarios <ref:2602.01342#pg1>.

Elias: Indeed, the combination of predictive modeling and reinforcement learning to optimize for URLLC latency while maximizing security is what makes this approach interesting from a cryptographic engineering standpoint <ref:2602.01342#pg1>. It’s a complex optimization problem that they tackle by framing it as a multi-objective evolutionary algorithm <ref:2602.01342#pg1>.

Priya: So, the main takeaway for me is that this isn't just about picking one PQC scheme; it’s about having an intelligent system manage the selection process dynamically based on what the environment is telling us right now <ref:2602.01342#pg1>. That adaptive planning mechanism is what really makes this paper stand out in terms of practical application to V2X security <ref:2602.01342#pg1>.

Nadia: It really does; the claim-driven evaluation on realistic V2X traces confirms that this adaptive framework is practically feasible, achieving lower switching frequency compared to other approaches like NSGA-II or RL-only ones <ref:2602.01342#pg1>. This suggests a path forward for deploying PQC in high-speed, latency-sensitive vehicular networks <ref:2602.01342#pg1>.

Elias: I think the implications point toward a future where cryptographic security isn't a static layer but an active, responsive component of the communication stack <ref:2602.01342#pg1>. It's about ensuring that even under noisy, fast-moving conditions, we maintain a high level of protection against quantum threats <ref:2602.01342#pg0>.

Priya: I think this is exciting because it moves the conversation from just 'can we use PQC?' to 'how do we make PQC work reliably in this incredibly dynamic, real-time vehicular context?' <ref:2602.01342#pg1>. It’s a step toward making quantum-safe communications a practical reality for autonomous systems <ref:2602.01342#pg1>.

Nadia: Absolutely. The adaptive planning for PQC selection under vehicular constraints, proposing APMOEA as a predictive multiobjective planning mechanism, is the central contribution here <ref:2602.01342#pg1>. It’s a very concrete way to address the challenges of post-quantum security in 6G V2X environments <ref:2602.01342#pg0>.

Paper summary: Elias: The secure execution via monotonic PQC transitions is another major contribution, designing that lightweight, authenticated transition protocol to enforce monotonic PQC upgrades <ref:2602.01342#pg1>. That’s a necessary piece of the puzzle for any adaptive system dealing with cryptographic changes <ref:2602.01342#pg1>.

Priya: So, we have this framework that uses context sensing and prediction to dynamically manage the selection of PQC configurations while maintaining formal guarantees on stability and bounded latency <ref:2602.01342#pg1>. That seems like a solid foundation for integrating quantum resistance into future vehicle communications <ref:2602.01342#pg1>.

Nadia: It is a solid foundation, Priya, and the experimental validation on LuST mobility and ERA5 data really gives us confidence in its practical feasibility <ref:2602.01342#pg1>. This paper shows that we can build systems that are both quantum-resilient and responsive to the physical realities of driving <ref:2602.01342#pg1>.

Elias: The work is compelling because it addresses the practical tension between achieving high security, managing computational overhead, and meeting extremely low latency requirements in a fast-evolving network environment <ref:2602.01342#pg1>. That balancing act is precisely what this adaptive framework is designed to handle <ref:2602.01342#pg1>.

Priya: I think the implications for broader deployment are that we can start designing V2X infrastructure with the expectation of such dynamic cryptographic management, rather than assuming a fixed security posture <ref:2602.01342#pg1>. It shifts the focus to managing the orchestration layer <ref:2602.01342#pg1>.

Nadia: That’s right; it shifts the focus to that adaptive orchestration of standardized PQC mechanisms rather than trying to invent entirely new cryptographic tools <ref:2602.01342#pg2>. It's about managing what we already have in a more intelligent way for the demands of 6G vehicular networks <ref:2602.01342#pg0>.

Elias: So, in summary, the paper "Adaptive Quantum-Safe Cryptography for 6G Vehicular Networks via Context-Aware Optimization" proposes APMOEA to dynamically select PQC configurations based on predicted context to optimize latency and security <ref:2602.01342#pg1>. It relies on formal guarantees like Decision Stability to ensure reliability under bounded prediction errors <ref:2602.01342#pg1>.

Priya: That seems like a very practical, layered approach to tackling the complex security challenges posed by future quantum computers in vehicular communication systems <ref:2602.01342#pg1>. I think we can expect to see more work focusing on the performance of these standardized PQC choices under such real-time adaptive pressures <ref:2602.01342#pg1>.

Nadia: We’ll be watching how these results translate into hardware implementations, because the engineering challenge of running this complex optimization engine in real-time is still significant <ref:2602.01342#pg1>. It’s a lot to digest, but it shows a clear path for making quantum-safe communication practical <ref:2602.01342#pg1>.

Conclusion: Elias: Well, it’s interesting how they aren't just picking one fixed scheme; they are using an APMOEA to manage the trade-off between latency and security across lattice-, code-, and hash-based options. The proof for Decision Stability is pretty solid, showing that as long as the prediction errors stay within a certain bound, the system stays on a stable choice.

Priya: From my side, I'm still focused on what the actual data shows; how does this dynamic switching affect data privacy when vehicles are moving and communicating so fast? The experimental traces they used with LuST mobility look intense.

Nadia: That’s exactly where I want to get to, Priya; we need to know if this adaptability actually makes a difference in terms of security posture under attack scenarios. Elias, you mentioned the parameters that could break it—what are the most sensitive inputs for that decision engine?

Elias: The most sensitive parts are definitely the context vector inputs like communication quality and weather conditions, because those can be manipulated to trick the predictor into choosing a less secure configuration. If an adversary can reliably spoof those environmental inputs, they might force the system into a suboptimal choice.

Priya: So you're saying the vulnerability isn't just in the PQC scheme itself, but in the context sensing and prediction pipeline that feeds it? That makes sense because that’s where we have to focus our privacy auditing efforts.

Nadia: Exactly; I want to know if someone could cheaply exploit this by crafting a specific sequence of channel noise or speed changes to force a downgrade or a less robust PQC signature. Elias, what about the secure transition protocol they designed? Does it mitigate those immediate risks during the switching process?

Elias: That protocol is designed to enforce authenticated version-monotonic negotiation, which should stop replay attacks and downgrade attempts during reconfiguration, assuming the key management within that protocol is sound. It’s a necessary piece for any adaptive system like this.

Priya: It sounds like the whole picture hinges on whether those formal guarantees translate into real-world resilience when faced with an attacker trying to exploit that transition window. The performance metrics they reported, like the sixty-five percent overhead reduction, suggest it’s quite practical for deployment.

Nadia: I agree; the fact that it achieves those latency and overhead cuts using reinforcement learning is compelling evidence that this isn't just theoretical work sitting on an arXiv page. Elias, what's your final word on whether these formal guarantees are strong enough to make you trust this framework for critical vehicle links?

Elias: The guarantees are strong under the specific conditions defined—namely, bounded prediction error—but we have to be cautious about how those bounds are set in a truly unpredictable chaotic environment. It’s a necessary trade-off between theoretical certainty and real-world uncertainty.

Priya: So the main implication is that for future V2X infrastructure, we should expect security to be managed by this intelligent orchestration layer rather than relying on a single, static PQC solution across all vehicles simultaneously <ref:2602.01342#pg1>.

Nadia: Precisely; this moves the conversation toward managing the orchestration of standardized PQC mechanisms in real-time, which is a much more realistic deployment scenario. We've seen how powerful this adaptive planning mechanism is when applied to these complex vehicular constraints <ref:2602.01342#pg1>.

University of Oslo

cs.CR, cs.AI, stat.AP

Submitted: 2026-02-01

Updated: 2026-02-01

Comments: Accepted for presentation at NDSS 2026 - FutureG Workshop, 23 February 2026. (10 pages, 5 figures.)

Journal ref: NDSS 2026 - FutureG Workshop

DOI: 10.14722/futureg.2026.240099

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 83/100

The gist: Powerful quantum computers may be able to break communication security for vehicles in 6G networks, necessitating new post-quantum cryptography methods that often introduce latency challenges.

Key concepts

ContextSensing Pipeline: This component gathers crucial environmental data from the vehicle, including its speed, communication quality, weather conditions, and message urgency. It combines these diverse inputs into a single 'context vector' that informs the decision-making process for selecting the best security algorithm.
Adaptive Predictive Multi-Objective Evolutionary Algorithm (APMOEA): This is the core decision engine that uses reinforcement learning to select the optimal cryptographic configuration. It balances multiple competing goals—such as low latency, low computational cost, and high security—by evaluating different PQC types against a weighted cost vector.
Secure Transition Protocol: This protocol manages the switching between different PQC algorithms securely. It ensures that when a change occurs, the system performs an authenticated version-monotonic negotiation to prevent attackers from forcing a downgrade or replaying old encrypted data.

Terminology

Elias — 24× in this paper · explained in 2 episodes
Nadia — 19× in this paper · explained in 2 episodes
Reinforcement Learning — 15× in this paper · explained in 4 episodes
Robustness — 15× in this paper · explained in 5 episodes
Trace — 14× in this paper · explained in 2 episodes
Communication Overhead — 12× in this paper · explained in 2 episodes
Adaptation — 9× in this paper · explained in 1 episode
Verification — 8× in this paper · explained in 2 episodes
Adaptive Planning — 6× in this paper · explained in 1 episode
Core — 5× in this paper · explained in 2 episodes
Computational overhead — 4× in this paper · explained in 2 episodes
Consistency — 4× in this paper · explained in 3 episodes
Abstraction — 3× in this paper · explained in 1 episode
Adversarial scenarios — 3× in this paper · explained in 1 episode
Adaptability — 2× in this paper · explained in 1 episode
Adaptive Predictive Multi-Objective Evolutionary Algorithm — 2× in this paper
Adaptive Selection — 2× in this paper · explained in 1 episode
Adversarial Context — 2× in this paper · explained in 1 episode
Benchmarking — 2× in this paper · explained in 3 episodes
Internet of Things — 2× in this paper
Objective Function — 2× in this paper · explained in 2 episodes
Predictive Modeling — 2× in this paper · explained in 2 episodes
Theoretical Guarantees — 2× in this paper · explained in 2 episodes
Transport Layer Security — 2× in this paper
Accuracy — 1× in this paper · explained in 1 episode
Actor — 1× in this paper · explained in 3 episodes
Adaptive Predictive Multi-Objective Evolutionary Algorithm (APMOEA) — 1× in this paper · explained in 1 episode
Context-Aware Adaptive PQC — 1× in this paper
Contextual Awareness — 1× in this paper · explained in 2 episodes
Formal Verification — 1× in this paper · explained in 2 episodes
Interpretability — 1× in this paper · explained in 15 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Q-learning — 1× in this paper · explained in 3 episodes
Reinforcement Learning (RL) — 1× in this paper · explained in 24 episodes
Reward Function — 1× in this paper · explained in 2 episodes
Steering — 1× in this paper · explained in 2 episodes

Summary

Powerful quantum computers may be able to break communication security for vehicles in 6G networks, necessitating new post-quantum cryptography methods that often introduce latency challenges. This paper proposes an adaptive framework that dynamically selects the most suitable lattice-, code-, or hash-based PQC configurations based on predicted mobility and channel variations using a predictive multiobjective evolutionary algorithm to meet stringent vehicular latency and security constraints.

The gist

The proposed Context-Aware Adaptive PQC (CAAP) framework dynamically selects the best cryptographic algorithm based on real-time sensing and predictive analysis to optimize for URLLC latency, compute cost, communication overhead, and quantum-resilience in 6G vehicular networks.

Framework Components

The CAAP framework consists of four main components designed to handle the dynamic nature of vehicular environments:

A ContextSensing Pipeline that collects vehicle speed, communication quality, weather conditions, and message urgency into a unified context vector.
A Short-Term Predictor that anticipates context changes in 100–200 ms windows using simple filters and regression models to provide stable data for decision-making.
An Adaptive Predictive Multi-Objective Evolutionary Algorithm (APMOEA) that balances constraints by selecting among lattice-based, code-based, or hash-based signatures based on a multi-dimensional cost vector: Calg = (Tenc, Tdec, Skey, Sct, Ecomp, Ssig).
A Secure Transition Protocol that enforces authenticated version-monotonic negotiation to prevent downgrade and replay attacks during reconfiguration.

Optimization Engine and Decision Making

The core decision engine is the APMOEA, which uses reinforcement learning (RL) to continually adapt and reduce processing delays while maximizing security. The objective function for the optimizer is defined by a multi-objective cost vector: f(a) = (Tlat(a), Ccomp(a), Scomm(a), σsec(a)), (3), where Tlat is end-to-end signing/verification latency, Ccomp is computational cost, Scomm captures key and payload size overhead, and σsec is cryptographic strength. The APMOEA seeks the Pareto optimal solution set under real-time constraints by using a fitness function that combines these factors: Fitness(a) = w1Tlat +w2Ccomp +w3Scomm −w4σsec.

Theoretical Guarantees for Stability and Security

The paper provides formal guarantees to ensure the framework's reliability. Theorem V.1 establishes Decision Stability, proving that "If Kε < ∆min, then APMOEA selects the same algorithm at for all t within a context-stable interval, meaning the system avoids unstable switching caused by noise or prediction errors. Theorem V.2 proves Monotonic Upgrade Security, asserting that no probabilistic polynomial-time adversary can induce a transition to any v < vt+1 or cause endpoint desynchronization through the secure transition protocol. Furthermore, Theorem V.3 demonstrates Latency Boundedness, showing that PQC latency remains URLLC-compliant because context drift is bounded, ensuring that Tlat(at, Xt) ≤ max a′∈A Tlat(a′, Xt) + O(δ)."

Experimental Validation and Performance

Extensive experiments using realistic traces—including LuST mobility, ERA5 weather data, and NR-V2X channel models—demonstrate significant performance gains. The framework reduces end-to-end latency by up to 27% and lowers communication overhead by up to 65%. Comparative analysis shows that APMOEA outperforms static baselines, achieving a lower switching frequency (4.3 switches per minute with RL) compared to NSGA-II or RL-only approaches, while maintaining security consistency across various hardware profiles. The secure transition protocol successfully prevents downgrade and replay attacks under adversarial scenarios.

Key Contributions

The main contributions of this work are:

: Adaptive planning for PQC selection under vehicular constraints, proposing APMOEA as a predictive multiobjective planning mechanism.

: Secure execution via monotonic PQC transitions, designing a lightweight, authenticated transition protocol that enforces monotonic PQC upgrades.

**: Formal guarantees for stability and bounded latency, establishing decision stability under bounded prediction error. **

**: Claim-driven evaluation on realistic V2X traces, demonstrating practical feasibility by reducing latency and stabilizing switching behavior using reinforcement learning. **

Scope of Deployment

The CAAP framework applies post-quantum cryptography at the application layer to protect V2V data communications transmitted over established 5G connections, distinct from network-layer security provided by 5G-AKA or EAP-TLS. The work focuses on the adaptive orchestration of standardized PQC mechanisms rather than introducing new cryptographic primitives.

Improvements for AI systems

As a fastidious researcher, I have analyzed the proposed Adaptive Quantum-Safe Cryptography (CAAP) framework for 6G vehicular networks. The core contribution is not in inventing new primitives, but in creating an intelligent orchestration layer that dynamically manages the trade-offs between quantum security and stringent Ultra-Reliable Low-Latency Communication (URLLC) constraints.

Here are specific improvements and the resulting capabilities of an AI system leveraging this paper:

)1. Dynamic PQC Configuration Orchestration Engine (DPCoe):

The system can implement a real-time, context-aware cryptographic selection module that replaces static security configurations with a predictive optimization engine (APMOEA).

It can ingest high-dimensional context vectors (SNR, PER, speed, acceleration, weather) and predict short-term evolution.
It dynamically selects the optimal PQC configuration (Lattice/Code/Hash) based on a multi-objective cost function that balances latency, computation energy, communication overhead (key/signature size), and required security level.

)2. Predictive Context Forecasting Subsystem:

The system can incorporate lightweight forecasting models to anticipate future channel conditions and mobility changes within a 100–200 ms window.

This allows the DPCoe to proactively select a PQC scheme that is expected to be optimal soon, minimizing decision latency.

)3. Reinforcement Learning (RL) Adaptive Weighting Module:

The system can utilize a tabular Q-learning approach integrated into the APMOEA loop to learn optimal dynamic weights for the multi-objective cost vector.

This module learns how to adjust the relative importance of latency, computation, and security objectives based on observed real-world performance (rewards), leading to lower switching frequencies and more stable PQC selections than traditional evolutionary algorithms.

)4. Secure Monotonic Transition Protocol:

The system can enforce a hardware-backed protocol that manages the switching between different PQC configurations.

It ensures that all upgrades are strictly monotonic, preventing downgrade attacks, replay attacks, and desynchronization by requiring authenticated version counters and fresh nonces at every transition point.

)Improved AI System Capabilities:

Can maintain URLLC compliance (latency < 20ms) even under high mobility and channel variability by avoiding computationally expensive PQC schemes when conditions are favorable, and selecting robust but lightweight schemes during adverse conditions (e.g., deep fades).
Can achieve up to a 27% reduction in end-to-end cryptographic latency compared to static PQC deployments by intelligently choosing the most efficient primitive for the current context.
Can maintain high security guarantees against quantum adversaries while ensuring system stability and preventing catastrophic security failures during cryptographic reconfiguration (downgrade/replay attacks).
Can operate autonomously in 6G V2X environments by continuously adapting its security posture based on real-time vehicular dynamics and environmental inputs, effectively managing the security vs. performance trade-off automatically.

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.