Transaction-based classification and detection approach for Ethereum smart contract. Issue 2 (March 2021)

• Record Type: Journal Article
• Authors: Hu, Teng; Liu, Xiaolei; Chen, Ting; Zhang, Xiaosong; Huang, Xiaoming; Niu, Weina; Lu, Jiazhong; Zhou, Kun; Liu, Yuan
• Is Part Of: Information processing & management. Volume 58:Issue 2(2021)
• Publication Date: 2021-03
• Subjects: Blockchain -- Ethereum -- Smart contract -- Classification -- Security; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102462
• Languages: English
• ISSNs: 0306-4573

Abstract

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.