Paper Info
Title
Transaction-based classification and detection approach for Ethereum smart contract
Abstract
Blockchain technology brings innovation to various industries. Ethereum is currently the second blockchain platform by market capitalization, it’s also the largest smart contract blockchain platform. Smart contracts can simplify and accelerate the development of various applications, but they also bring some problems. For example, smart contracts are used to commit fraud, vulnerability contracts are deliberately developed to undermine fairness, and there are numerous duplicative contracts that waste performance with no actual purpose. In this paper, we propose a transaction-based classification and detection approach for Ethereum smart contract to address these issues. We collected over 10,000 smart contracts from Ethereum and focused on the data behavior generated by smart contracts and users. We identified four behavior patterns from the transactions by manual analysis, which can be used to distinguish the difference between different types of contracts. Then 14 basic features of a smart contract are constructed from these. To construct the experimental dataset, we propose a data slicing algorithm for slicing the collected smart contracts. After that, we use an LSTM network to train and test our datasets. The extensive experimental results show that our approach can distinguish different types of contracts and can be applied to anomaly detection and malicious contract identification with satisfactory precision, recall, and f1-score.
Year
DOI
Venue
2021
10.1016/j.ipm.2020.102462
Information Processing & Management
Keywords
DocType
Volume
Blockchain,Ethereum,Smart contract,Classification,Security
Journal
58
Issue
ISSN
Citations 
2
0306-4573
4
PageRank 
References 
Authors
0.40
32
9
Name
Order
Citations
PageRank
Teng Hu172.85
Xiaolei Liu240.73
Ting Chen3777.68
Xiaosong Zhang49114.00
Xiaoming Huang541.07
Weina Niu652.09
Jiazhong Lu752.12
Kun Zhou841.41
Yuan Liu971.16