发表机构
G Innovation Centre, Institute for Communication Systems, University of Surrey, UK（Surrey大学6G创新中心，通信系统研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对电信/物联网欺诈控制请求，将其重构为区块链关联可审计决策管理。采用QLoRA调整大语言模型分支及多种方法评分、决策，经实验验证不同方法效果，分析区块链遥测数据，揭示相关指标差异原因。
AI 中文摘要
电信欺诈控制研究通常止于探测器级分类，但部署使用需要请求级策略解析、生命周期可追溯性和可审计性。本文将欺诈控制重新构建为针对合成电信/物联网欺诈控制请求的区块链关联可审计决策管理。主要结果是，QLoRA调整的大语言模型分支比零样本提示更实用，但主要接近而非超越低成本集中集成。框架将每个合成部署记录映射到管理请求，通过确定性硬欺诈门阻止明确的越界情况，使用集中式机器学习、联邦元学习或大语言模型家族风险源对非硬请求评分，并通过共享的五态策略、两区细化机制和本地以太坊兼容审计层解决行动。评估使用单独的合成训练数据和10万条记录的部署重放语料库，应视为受控漂移重放证据。验证时，M1平衡最强，合法请求误报率在0.10操作上限下为0.0890，软欺诈召回率为0.8341。在标记部署重放中，合法误报率差距变大：M1升至0.1646，M3-QLoRA升至0.1801，而M3-QLoRA将M3-基础合法误报率从0.3915降低，软欺诈召回率达到0.8240。区块链遥测表明，生命周期的气体、成本、延迟和吞吐量差异由提交的链下决策配置文件驱动，而非欺诈逻辑变化。
英文摘要
Artificial intelligence (AI)-enabled security decision systems in telecom and IoT networks can draw on heterogeneous models whose outputs may trigger operational actions. Recording such decisions on a blockchain does not establish that they are authorised, applicable, policy-consistent, or still valid at execution time. This paper presents governance-2, an authority-bound and fail-closed architecture that separates upstream scientific decision formation from downstream operational enforcement. Each governed case is bound to registered dataset, model, policy, deployment, and optional refiner authorities. Smart-contract checks enforce role separation, authority compatibility, score-to-state and state-to-action consistency, lifecycle validity, replay protection, pause control, authority revocation, and exact-action execution. Evaluation uses two independent branches: a controlled spectrum-access replay with four frozen heterogeneous decision configurations and a measured radio-frequency branch based on WiFiSpectralJam. Across eight frozen measured-data decision streams, governance-2 processes 153,744 stream-case instances derived from 19,218 measured captures while preserving interference-specific semantics and unresolved review states. The full contract rejects all tested invalid operations; stateful invariant testing completes 2,000 generated transaction sequences with zero invariant violations; and single-capability ablation shows that removing an enforcement family exposes its assigned invalid operations while unrelated protections remain active. The results indicate that heterogeneous AI security decisions can share a common governance plane across telecom and IoT settings without redefining their scientific semantics.
Comments30 pages, 4 figures, 8 tables
