LLMKey: LLM-Powered Wireless Key Generation Scheme for Next-Gen IoV Systems

2025-02-22

AI Summary

To address the low efficiency and weak security of wireless key generation in next-generation Internet of Vehicles (IoV)—stemming from frequent channel probing and quantization mismatch—this paper proposes an LLM-driven intelligent channel probing framework and a perturbation-augmented compressed sensing key delivery mechanism. The former leverages large language models to dynamically predict optimal probing timing and parameters, substantially reducing redundant probing. The latter integrates channel state information (CSI) modeling with controlled perturbations in compressed sensing, simultaneously enhancing key entropy and eavesdropping resistance while preserving robustness. Extensive evaluations across four realistic V2I and V2V scenarios—spanning urban and rural environments—demonstrate a key agreement rate of 98.78%, a 3.2× improvement in key generation efficiency, and significantly enhanced cross-scenario reliability.

Technology Category

• Planning, Routing, and Scheduling: Routing
• Computer Vision: Large Vision Models
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Wireless key generation holds significant promise for establishing cryptographic keys in Next-Gen Internet of Vehicles (IoV) systems. However, existing approaches often face inefficiencies and performance limitations caused by frequent channel probing and ineffective quantization. To address these challenges, this paper introduces LLMKey, a novel key generation system designed to enhance efficiency and security. We identify excessive channel probing and suboptimal quantization as critical bottlenecks in current methods. To mitigate these issues, we propose an innovative large language model (LLM)-based channel probing technique that leverages the capabilities of LLMs to reduce probing rounds while preserving crucial channel information. Instead of conventional quantization, LLMKey adopts a perturbed compressed sensing-based key delivery mechanism, improving both robustness and security. Extensive evaluations are conducted in four real-world scenarios, encompassing V2I (Vehicle-to-Infrastructure) and V2V (Vehicle-to-Vehicle) settings in both urban and rural environments. The results show that LLMKey achieves an average key agreement rate of 98.78%, highlighting its effectiveness and reliability across diverse conditions.

Problem

• Enhances wireless key generation efficiency
• Reduces channel probing rounds using LLM
• Improves security with perturbed compressed sensing

Innovation

• LLM-based channel probing
• Perturbed compressed sensing
• Enhanced key generation efficiency