Multimodal fusion for enhanced smart contract reputability analysis in blockchain

Abstract

This study explores the limitations of traditional smart contract reputability assessments, which often rely on either static code analysis or isolated transactional data, missing a comprehensive view of a contract’s evolving trustworthiness. Motivated by the need for robust reputability evaluation in blockchain ecosystems, this work introduces a mul‐ timodal data fusion framework to integrate static and dynamic data sources. Existing solutions are effective at anomaly detection and vulnerability analysis but fail to com‐ bine these data types for holistic insights. Our proposed framework employs boosting algorithms with GAN‐based augmentation for opcode embeddings, achieving superior performance in identifying illicit contracts, with a LightGBM model delivering 97.67% accuracy and a recall of 0.942. A CNN‐based autoencoder is incorporated for multi‐ modal anomaly detection, effectively identifying abnormal patterns by leveraging the interplay between static code and transactional behavior. The multimodal integration yielded a 7.25% improvement in recall compared to single‐source models, confirming its enhanced capacity to detect reputability shifts and abnormal behavior. For long‐term monitoring, an LSTM model captures reputability trends, demonstrating low validation loss and minimal prediction lag, ensuring timely and accurate identification of evolving trustworthiness. The results highlight that multimodal fusion significantly enhances pre‐ dictive accuracy, robust anomaly detection, and the ability to model reputability trends, offering a powerful tool for early risk detection and proactive intervention strategies. This research advances decentralized application security by providing a reliable frame‐ work for improving trustworthiness and mitigating potential risks, forming the crux of a sophisticated multimodal data fusion strategy.