发表机构
Faculty of Data Science, City University of Macau; Guangzhou University（澳门城市大学数据科学学院; 广州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对区块链物联网中合约与设备层逻辑漏洞，提出多智能体异构图注意力框架MA-HGAT，统一建模多类工件，支持多种检测任务并实现轻量级边缘部署。
AI 中文摘要
区块链物联网系统将智能合约与嵌入式设备集成，以支持去中心化的设备管理和访问控制。因此，其安全性共同依赖于链上合约的逻辑和链下设备固件的逻辑。任一层中的逻辑缺陷都可能违反相同的系统不变量，例如未经授权的访问、不当的状态更改或未受保护的特权操作。现有方法依赖于合约分析、固件分析和基于图的漏洞检测。然而，这些方法通常关注单一层或单一工件，并且往往依赖于预定义的漏洞模式、仿真保真度或掩盖安全相关组件角色的同质表示。它们还缺乏支持不同安全任务的统一架构，同时可部署在资源受限的网关上。为解决这些限制，我们将MA-HGAT扩展为跨层多智能体异构图注意力框架，该框架使用统一的四角色、九关系模式对合约、固件工件、设备群和交易流进行建模。角色对齐的智能体通过交叉注意力交换异构证据，而图级、链路级和节点级头部支持多种检测任务，基于角色的网关-云分区支持轻量级边缘推理。因此，MA-HGAT为检测区块链物联网系统中合约与设备层的逻辑漏洞提供了一个统一且可部署的框架。
英文摘要
Blockchain-enabled Internet of Things (IoT) systems integrate smart contracts with embedded devices to support decentralized device management and access control. Their security therefore depends jointly on the logic of on-chain contracts and off-chain device firmware. Logic flaws in either layer can violate the same system invariants, such as unauthorized access, improper state changes, or unguarded privileged operations. Existing approaches rely on contract analysis, firmware analysis, and graph-based vulnerability detection. However, these methods typically focus on a single layer or artifact and often depend on predefined vulnerability patterns, emulation fidelity, or homogeneous representations that obscure security-relevant component roles. They also lack a unified architecture that supports different security tasks while remaining deployable on resource-constrained gateways. To address these limitations, we extend MA-HGAT into a cross-layer multi-agent heterogeneous graph attention framework that models contracts, firmware artifacts, device fleets, and transaction streams with a unified four-role, nine-relation schema. Role-aligned agents exchange heterogeneous evidence through cross-attention, while graph-, link-, and node-level heads support multiple detection tasks and a role-based gateway--cloud partition enables lightweight edge inference. MA-HGAT thus provides a unified and deployable framework for detecting logic vulnerabilities across the contract and device layers of blockchain-enabled IoT systems.
