Cybersecurity-aware decentralized machine learning framework for construction equipment motion recognition using blockchain

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.