Enhanced Smart Contract Reputability Analysis using Multimodal Data Fusion on Ethereum

2025-03-21

AI Summary

To address the fragmentation between static code analysis and dynamic transaction data—and the resulting difficulty in adapting to evolving smart contract risks—in Ethereum trustworthiness assessment, this paper proposes a multimodal, interpretable analytical framework. Methodologically, it introduces a GAN-enhanced opcode embedding scheme to mitigate class imbalance; designs a trust-centered fusion strategy integrating static features (e.g., control/data flow) with dynamic representations (e.g., transaction graphs and temporal behavioral patterns); and constructs an end-to-end multimodal fusion model. Experimental results demonstrate that the framework achieves 97.67% accuracy and 0.942 recall in malicious contract detection—outperforming unimodal baselines by 7.25% in accuracy. Moreover, it exhibits strong generalization capability, enabling early-risk identification and providing human-interpretable risk alerts. This work establishes a novel, robust, and explainable paradigm for trustworthy smart contract evaluation in decentralized ecosystems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Machine Learning: Evaluation and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Cryptocurrency and smart contracts

Abstract

The evaluation of smart contract reputability is essential to foster trust in decentralized ecosystems. However, existing methods that rely solely on static code analysis or transactional data, offer limited insight into evolving trustworthiness. We propose a multimodal data fusion framework that integrates static code features with transactional data to enhance reputability prediction. Our framework initially focuses on static 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 static 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.

Problem

• Enhancing smart contract reputability prediction using multimodal data fusion
• Addressing class imbalance in illicit contract detection with GAN-augmented embeddings
• Improving recall by integrating static and transactional data for trust assessment

Innovation

• Multimodal fusion of static and transactional data
• GAN-augmented opcode embeddings for imbalance
• Reputability-centric fusion strategy boosts recall