摘要

人工智能（AI）在建筑业的应用日益广泛，可提升生产效率、减少安全事故并优化协作效率。但针对 AI 系统的攻击会带来网络安全威胁，可能导致设备损坏、经济损失、运营中断、安全事故乃至人员伤亡等严重后果。由于建筑业对 AI 网络安全漏洞的防御意识薄弱且 IT 资源不足，相关防御工作进展有限。为此，本文提出一种具备网络安全意识的去中心化机器学习（CADML）框架，利用区块链保护机器学习（ML）模型全生命周期的网络安全。首先，介绍 CADML 框架的工作流程，阐明区块链与机器学习的融合逻辑；其次，开发一种新的区块链智能合约算法 ——ML 嵌入智能合约（MLSC），以去中心化方式实现 AI 的训练与应用。该框架的核心创新在于，将现有 “部分性” 区块链 - 机器学习融合方法拓展为支持机器学习 “全生命周期”（从原始数据存储、模型训练、部署应用到更新迭代）在去中心化且安全的区块链环境中运行。通过建筑设备动作识别测试表明：（1）机器学习模型可在区块链中成功完成训练与部署；（2）模型性能（准确率、精确率、召回率）达到可接受水平。

Artificial intelligence (AI) is playing an increasing role in the construction industry to enhance productivity, reduce safety accidents, and optimize collaboration efficiency. However, attacks on AI systems also introduce cybersecurity threats that could lead to severe consequences, such as equipment damage, financial loss, operational downtime, safety accidents, and potential loss of life. Motivated by the construction industry’s limited efforts to defend against AI cybersecurity vulnerabilities—a result of a lack of awareness and IT resources—this paper aims to propose a cybersecurity-aware decentralized machine learning (CADML) framework to protect the life cycle cybersecurity of machine learning (ML) models leveraging blockchain. First, the workflow of the CADML framework will be introduced to illustrate the logic of blockchain-ML integration. Second, a new blockchain smart contract algorithm, ML-embed smart contract (MLSC), will be developed to train and apply AI in a decentralized manner. The primary innovation framework extends current “partially” blockchain-ML integration methods to enable the ML’s “lifecycle” (from raw data storage, training, implementation, to model update) to operate in a decentralized and secure blockchain environment. The framework is tested to recognize construction equipment motions. Results show that (1) the ML model could be successfully trained and implemented within a blockchain and (2) the ML performance (accuracy, precision, and recall) is acceptable.

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