Blockchain Smart Contract Classification Method Based on Double Siamese Neural Network

GUO Jiashu; WANG Qi; LI Zeya; WU Mengde; ZHANG Hongxia

doi:10.11999/JEIT230185

Volume 46 Issue 3

Mar. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 1060-1068

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

Citation:

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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Blockchain Smart Contract Classification Method Based on Double Siamese Neural Network

doi: 10.11999/JEIT230185

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

Funds: The Major Scientific and Technological Projects of CNPC (ZD2019-183-004), The Fundamental Research Funds for the Central Universities (20CX05019A)

Received Date: 2023-03-22
Rev Recd Date: 2023-09-20

Available Online: 2023-10-07

Publish Date: 2024-03-27

Abstract

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.
- Smart contract,
- Blockchain,
- Siamese network,
- Ethereum

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References(25)

References

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