Semi-Supervised Learning and Blockchain Integration for Transparent Smart Contract Security

Abstract

With the rapid proliferation of blockchain technology and smart contracts in consumer IoT systems, ensuring digital trust and security remains a persistent challenge due to vulnerability diversity, data scarcity, and limited on-chain auditability. To address these issues, this paper presents PureChain, a scalable trust framework that integrates semi-supervised deep learning for smart contract vulnerability detection with blockchain-based audit logging for transparent security assurance. The framework employs a lightweight Conv1D neural architecture trained via iterative pseudo-labeling and a Mean Teacher strategy, effectively leveraging tens of thousands of unlabeled contracts to minimize annotation requirements. Experimental evaluation shows that PureChain achieves macro F1-scores exceeding 99% and significant improvements in recall for rare vulnerabilities, outperforming both supervised and state-of-the-art baselines. The integration of blockchain ensures all detection events are immutably recorded, enabling accountable and verifiable device operation. These results demonstrate that adaptive semi-supervised learning, combined with on-chain transparency, provides a robust and efficient foundation for secure smart contract monitoring across IoT and edge environments, with future work targeting more adaptive pseudo-labeling, semi supervised learning (SSL) techniques and cross-chain generalization.