Smart contract vulnerability detection method based on Bi-modal cross-attention mechanism

CHEN Jinfu; HU Xinyi; CAI Saihua; MIN Xirun

doi:10.11959/j.issn.1000-436x.2025107

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Smart contract vulnerability detection method based on Bi-modal cross-attention mechanism

Papers | 更新时间：2025-07-04

- Smart contract vulnerability detection method based on Bi-modal cross-attention mechanism
- Journal on Communications Vol. 46, Issue 6, Pages: 218-232(2025)
- 作者机构：
  
  1.江苏大学计算机科学与通信工程学院，江苏镇江 212013
  2.江苏省工业网络安全技术重点实验室，江苏镇江 212013
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62172194;62202206;U1836116);The Qinglan Project of Jiangsu Province
- DOI：10.11959/j.issn.1000-436x.2025107
  CLC： TP311
- Received：10 March 2025，
  
  Revised：2025-06-03，
  
  Published：25 June 2025
- 稿件说明：
移动端阅览
陈锦富,胡心怡,蔡赛华等.基于双模态交叉注意力机制的智能合约漏洞检测方法[J].通信学报,2025,46(06):218-232.

CHEN Jinfu,HU Xinyi,CAI Saihua,et al.Smart contract vulnerability detection method based on Bi-modal cross-attention mechanism[J].Journal on Communications,2025,46(06):218-232.
陈锦富,胡心怡,蔡赛华等.基于双模态交叉注意力机制的智能合约漏洞检测方法[J].通信学报,2025,46(06):218-232. DOI： 10.11959/j.issn.1000-436x.2025107.

CHEN Jinfu,HU Xinyi,CAI Saihua,et al.Smart contract vulnerability detection method based on Bi-modal cross-attention mechanism[J].Journal on Communications,2025,46(06):218-232. DOI： 10.11959/j.issn.1000-436x.2025107.

摘要

针对智能合约漏洞检测中现有深度学习方法依赖单一模态进行特征提取、对上下文信息捕获不足导致检测准确率较低的问题，提出了一种基于双模态交叉注意力机制的智能合约漏洞检测方法，设计了特定的注意力机制，同时分析合约的源代码和字节码，实现源代码中的高级语义特征与字节码中的底层执行流程双向映射和互补增强，丰富特征表示。引入的残差连接有效地保持和传递原始特征信息，缓解深层网络训练中的梯度消失问题。在公开数据集上进行广泛测试，实验结果表明，所提方法相较基线提高了检测准确率2%以上；消融实验结果显示，跨模态特征融合和注意力机制的设计相互协同，显著提升检测性能。

Abstract

To address the problem that existing deep learning methods for smart contract vulnerability detection rely on single-modal feature extraction and insufficient contextual information capture

leading to relatively low detection accuracy

a smart contract vulnerability detection method based on the Bi-modal cross-attention mechanism was proposed. A specific attention mechanism was designed that simultaneously analyzed both contract source code and bytecode

achieving bidirectional mapping and complementary enhancement between high-level semantic features in source code and low-level execution flows in bytecode

thereby enriching feature representation. Residual connections were introduced to effectively preserve and transmit original feature information

mitigating the vanishing gradient problem in deep network training. Extensive testing on public datasets demonstrates that the proposed method improves detection accuracy by more than 2% compared to baselines. Ablation experiments confirm that cross-modal feature fusion and the design of the attention mechanism work in synergy with each other

significantly improving the detection performance.

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references

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