Honeypot contract detection of blockchain based on deep learning

Hongxia ZHANG; Qi WANG; Dengyue WANG; Ben WANG

doi:10.11959/j.issn.1000-436x.2022011

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Honeypot contract detection of blockchain based on deep learning

Papers | 更新时间：2024-06-05

- Honeypot contract detection of blockchain based on deep learning
- Journal on Communications Vol. 43, Issue 1, Pages: 194-202(2022)
- 作者机构：
  
  中国石油大学（华东）青岛软件学院、计算机科学与技术学院，山东青岛 266580
- 作者简介：
- 基金信息：
  
  The Major Scientific and Technological Projects of CNPC(ZD2019-183-004);The Fundamental Research Funds for the Central Universities(20CX05019A)
- DOI：10.11959/j.issn.1000-436x.2022011
  CLC： TP309.2
- Online First：2022-01，
  
  Published：25 January 2022
- 稿件说明：
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Hongxia ZHANG, Qi WANG, Dengyue WANG, et al. Honeypot contract detection of blockchain based on deep learning[J]. Journal on Communications, 2022, 43(1): 194-202.
DOI：

Hongxia ZHANG, Qi WANG, Dengyue WANG, et al. Honeypot contract detection of blockchain based on deep learning[J]. Journal on Communications, 2022, 43(1): 194-202. DOI： 10.11959/j.issn.1000-436x.2022011.

摘要

针对当前检测方法准确率不高以及模型泛化性较差的问题，提出了基于 KOLSTM 深度学习模型的蜜罐陷阱合约检测方法。首先，通过分析蜜罐陷阱合约的特点，提出了关键操作码的概念，并设计了可用于选取智能合约中关键操作码的关键词提取方法；其次，在传统的LSTM模型中加入关键操作码权重机制，构建了可以同时捕获蜜罐陷阱合约中隐藏的序列特征以及关键操作码特征的 KOLSTM 模型。最后，通过实验表明，该模型具有较高的识别精确率，在二分类和多分类检测场景下的F值较LightGBM模型分别提升2.39%与19.54%。

Abstract

Aiming at the problems of low accuracy of current detection methods and poor generalization of model

a honeypot contract detection method based on KOLSTM deep learning model was proposed.Firstly

by analyzing the characteristics of honeypot contract

the concept of key opcode was proposed

and a keyword extraction method which could be used to select the key opcode in smart contract was designed.Secondly

by adding the key opcode weight mechanism to the traditional LSTM model

a KOLSTM model which could simultaneously capture the sequence features and key opcode features hidden in the honeypot contract was constructed.Finally

the experimental results show that the model had a high recognition accuracy.Compared with the existing methods

the F-score is improved by 2.39% and 19.54% respectively in the two classification and multi-classification detection scenes.

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Keywords

references

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