融合代码与交易数据，提升以太坊智能合约可信度评估准确率
Enhanced Smart Contract Reputability Analysis using Multimodal Data Fusion on Ethereum
- 用GAN增强的字节码嵌入分析代码特征，解决样本不平衡问题
- 结合代码与交易数据后召回率提升7.25%，达0.942
- 适合区块链安全、智能合约审计方向的研究者与从业者
智能合约可信度评估对构建去中心化生态的信任至关重要。现有仅依赖代码或交易数据的方法难以捕捉动态信任变化。本文提出一种多模态数据融合框架，整合代码特征与交易数据以增强可信度预测。首先通过基于AI的代码分析，利用GAN增强的字节码嵌入处理类别不平衡，在检测非法合约上达到97.67%准确率与0.942召回率，优于传统过采样方法。在此基础上，构建以可信度为核心的融合策略，结合代码与交易数据使召回率比单一来源模型提升7.25%，在多个验证集上表现稳健。该方法提供合约行为的全局视图，显著提升对欺诈行为的识别与异常模式的预测能力，有助于更精准评估可信度、主动防控风险并强化区块链安全。
原文摘要 · Abstract (English)
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.
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