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

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.