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A Bytecode-based Approach for Smart Contract Classification
conference contribution
posted on 2022-12-06, 03:09 authored by C Shi, Yong XiangYong Xiang, J Yu, Longxiang GaoLongxiang Gao, Keshav SoodKeshav Sood, Robin Ram Mohan DossRobin Ram Mohan DossWith the development of blockchain technologies, the number of smart contracts deployed on blockchain platforms is growing exponentially, which makes it difficult for users to find desired services by manual screening. The automatic classification of smart contracts can provide blockchain users with keyword-based contract searching and helps to manage smart contracts effectively. Current research on smart contract classification focuses on Natural Language Processing (NLP) solutions which are based on contract source code. However, more than 94% of smart contracts are not open-source, so the application scenarios of NLP methods are very limited. Meanwhile, NLP models are vulnerable to adversarial attacks. This paper proposes a classification model based on features from contract bytecode instead of source code to solve these problems. We also use feature selection and ensemble learning to optimize the model. Our experimental studies on over 11K real-world Ethereum smart contracts show that our model can classify smart contracts without source code and has better performance than baseline models. Our model also has good resistance to adversarial attacks compared with NLP-based models. In addition, our analysis reveals that account features used in many smart contract classification models have little effect on classification and can be excluded.
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Volume
00Pagination
1046-1054Location
ELECTR NETWORKPublisher DOI
Start date
2022-03-15End date
2022-03-18ISSN
1944-2793ISBN-13
9781665437868Language
EnglishPublication classification
E1 Full written paper - refereedTitle of proceedings
Proceedings - 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2022Event
29th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)Publisher
IEEE COMPUTER SOCSeries
European Conference on Software Maintenance and ReengineeringUsage metrics
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