Enhanced Smart Contract Reputability Analysis using Multimodal Data Fusion on Ethereum
This work addresses the need for more accurate reputability assessments in decentralized ecosystems like Ethereum, though it is incremental as it builds on existing methods by combining data sources.
The paper tackled the problem of evaluating smart contract reputability by proposing a multimodal data fusion framework that integrates code features with transactional data, achieving 97.67% accuracy and a 7.25% recall improvement over single-source models.
The evaluation of smart contract reputability is essential to foster trust in decentralized ecosystems. However, existing methods that rely solely on code analysis or transactional data, offer limited insight into evolving trustworthiness. We propose a multimodal data fusion framework that integrates code features with transactional data to enhance reputability prediction. Our framework initially focuses on AI-based code analysis, utilizing GAN-augmented opcode embeddings to address class imbalance, achieving 97.67% accuracy and a recall of 0.942 in detecting illicit contracts, surpassing traditional oversampling methods. This forms the crux of a reputability-centric fusion strategy, where combining code and transactional data improves recall by 7.25% over single-source models, demonstrating robust performance across validation sets. By providing a holistic view of smart contract behaviour, our approach enhances the model's ability to assess reputability, identify fraudulent activities, and predict anomalous patterns. These capabilities contribute to more accurate reputability assessments, proactive risk mitigation, and enhanced blockchain security.