| 基于双层孪生神经网络的区块链智能合约分类方法 |
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| 引用本文： | 郭加树, 王琪, 李择亚, 武梦德, 张红霞. 基于双层孪生神经网络的区块链智能合约分类方法[J]. 电子与信息学报, 2024, 46(3): 1060-1068. doi: 10.11999/JEIT230185 |
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| 作者姓名： | 郭加树 王琪 李择亚 武梦德 张红霞 |
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| 作者单位： | 1.中国石油大学(华东)青岛软件学院 青岛 266580;;2.中国石油大学(华东)计算机科学与技术学院 青岛 266580 |
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| 基金项目： | 中央高校基本科研业务费专项资金(20CX05019A)~~； |
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| 摘 要： | 当前通过深度学习方法进行区块链智能合约分类的方法越来越流行，但基于深度学习的方法往往需要大量的样本标签数据去进行有监督的模型训练，才能达到较高的分类性能。该文针对当前可用智能合约数据集存在数据类别不均衡以及标注数据量过少会导致模型训练困难，分类性能不佳的问题，提出基于双层孪生神经网络的小样本场景下的区块链智能合约分类方法：首先，通过分析智能合约数据特征，构建了可以捕获较长合约数据特征的双层孪生神经网络模型；然后，基于该模型设计了小样本场景下的智能合约训练策略和分类方法。最后，实验结果表明，该文所提方法在小样本场景下的分类性能优于目前最先进的智能合约分类方法，分类准确率达到94.7%，F1值达到94.6%，同时该方法对标签数据的需求更低，仅需同类型其他方法约20%数据量。
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| 关 键 词： | 智能合约 区块链 孪生网络 以太坊 |
| 收稿时间： | 2023-03-22 |
| 修稿时间： | 2023-09-20 |
Blockchain Smart Contract Classification Method Based on Double Siamese Neural Network |
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| GUO Jiashu, WANG Qi, LI Zeya, WU Mengde, ZHANG Hongxia. Blockchain Smart Contract Classification Method Based on Double Siamese Neural Network[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1060-1068. doi: 10.11999/JEIT230185 |
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| Authors: | GUO Jiashu WANG Qi LI Zeya WU Mengde ZHANG Hongxia |
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| Affiliation: | 1. Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China;;2. College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China |
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| Abstract: | At present, methods for classifying blockchain smart contracts using deep learning methods are becoming increasingly popular. However, methods based on deep learning often require a large amount of sample label data for supervised model training to achieve high classification performance. A blockchain smart contract classification method based on a two-level twin neural network in a small sample scenario is proposed to address the problem that currently available smart contract datasets have uneven data categories and insufficient labeled data volumes, which can lead to difficulty in model training and poor classification performance. Firstly, by analyzing the characteristics of smart contract data, a two-level twin neural network model that can capture the characteristics of longer contract data is constructed; Then, based on this model, a training strategy and classification method for smart contracts in small sample scenarios are designed. Finally, experimental results show that the classification performance of the proposed method in this paper is superior to the most advanced smart contract classification methods in small sample scenarios, with a classification accuracy of 94.7% and an F1 value of 94.6%. At the same time, this method requires less tag data, requiring only about 20% data from other methods of the same type. |
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| Keywords: | Smart contract Blockchain Siamese network Ethereum |
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