Honeypot contract detection method for Ethereum based on source code structure and graph attention network

Youwei WANG; Yudong HOU; Lizhou FENG

doi:10.11959/j.issn.1000-436x.2023178

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Honeypot contract detection method for Ethereum based on source code structure and graph attention network

Papers | 更新时间：2024-06-06

- Honeypot contract detection method for Ethereum based on source code structure and graph attention network
- Journal on Communications Vol. 44, Issue 9, Pages: 161-172(2023)
- 作者机构：
  
  1. 中央财经大学信息学院，北京 102206
  2. 天津财经大学统计学院，天津 300222
- 作者简介：
- 基金信息：
  
  The Ministry of Education of Humanities and Social Science Project(19YJCZH178);The National Natural Science Foundation of China(61906220);The National Social Science Foundation of China(18CTJ008)
- DOI：10.11959/j.issn.1000-436x.2023178
  CLC： TP309.2
- Online First：2023-09，
  
  Published：25 September 2023
- 稿件说明：
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Youwei WANG, Yudong HOU, Lizhou FENG. Honeypot contract detection method for Ethereum based on source code structure and graph attention network[J]. Journal on Communications, 2023, 44(9): 161-172.
DOI：

Youwei WANG, Yudong HOU, Lizhou FENG. Honeypot contract detection method for Ethereum based on source code structure and graph attention network[J]. Journal on Communications, 2023, 44(9): 161-172. DOI： 10.11959/j.issn.1000-436x.2023178.

摘要

针对目前蜜罐合约检测方法准确率不高以及泛化性较差等问题，提出了基于源码结构和图注意力网络的以太坊蜜罐合约检测（CSGDetector）方法。首先，为了提取出智能合约Solidity源码的结构信息，对源码进行语法分析，将其转换为XML解析树；然后，筛选出可以表达合约结构特征和内容特征的特征词集，并构造出合约源码结构图；最后，为避免数据集不平衡性带来的影响，在集成学习理论基础上引入教师模型和学生模型的概念，分别从全局和局部的角度训练图注意力网络模型，并融合所有模型的输出作为合约最终检测结果。实验表明，与已有方法KOLSTM相比，CSGDetector在二分类与多分类实验中的F

值分别提升了1.27%与7.21%，验证了其具有较高的蜜罐检测能力；与已有方法XGB相比，CSGDetector在掩蔽蜜罐检测实验中针对不同类型蜜罐合约的平均召回率提升了7.57%，验证了所提方法在提升算法泛化性能方面的有效性。

Abstract

To address the problems of low accuracy and poor generalization of current honeypot contract detection methods

a honeypot contract detection method for Ethereum based on source code structure and graph attention network was proposed.Firstly

in order to extract the structural information of the Solidity source code of the smart contract

the source code was parsed and converted into an XML parsing tree.Then

a set of feature words that could express the structural and content characteristics of the contract was selected

and the contract source code structure graph was constructed.Finally

in order to avoid the impact of dataset imbalance

the concepts of teacher model and student model were introduced based on th

e ensemble learning theory.Moreover

the graph attention network model was trained from the global and local perspectives

respectively

and the outputs of all models were fused to obtain the final contract detection result.The experiments demonstrate that CSGDetector has higher honeypot detection capability than the existing method KOLSTM

with increments of 1.27% and 7.21% on F

measurement in two-class classification and multi-class classification experiments

respectively.When comparing with the existing method XGB

the average recall rate of CSGDetector in the masked honeypot detection experiments for different types of honeypot contracts is improved by 7.57%

which verifies the effectiveness of the method in improving the generalization performance of the algorithm.

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references

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