基于深度学习的区块链蜜罐陷阱合约检测
Honeypot contract detection of blockchain based on deep learning
- 通信学报 2022年43卷第1期 页码：194-202
- 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：
[ "张红霞（1981- ），女，山东东营人，博士，中国石油大学（华东）副教授、硕士生导师，主要研究方向为边缘计算、区块链技术、服务计算等" ]
[ "王琪（1997- ），男，山东枣庄人，中国石油大学（华东）硕士生，主要研究方向为区块链技术、数据挖掘" ]
[ "王登岳（1996- ），男，山东聊城人，中国石油大学（华东）硕士生，主要研究方向为网络与服务计算" ]
[ "王奔（1997- ），男，山东临沂人，中国石油大学（华东）硕士生，主要研究方向为计算机视觉、多目标跟踪" ]
- 基金信息：中石油重大科技基金资助项目(ZD2019-183-004);中央高校基本科研业务费专项资金资助项目(20CX05019A)
DOI：10.11959/j.issn.1000-436x.2022011
中图分类号： TP309.2-
网络首发：2022-01，
纸质出版：2022-01-25
- 稿件说明：
移动端阅览
张红霞, 王琪, 王登岳, 等. 基于深度学习的区块链蜜罐陷阱合约检测[J]. 通信学报, 2022,43(1):194-202.
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.
张红霞, 王琪, 王登岳, 等. 基于深度学习的区块链蜜罐陷阱合约检测[J]. 通信学报, 2022,43(1):194-202. DOI： 10.11959/j.issn.1000-436x.2022011.
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.
关键词
Keywords
references
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