【Author】
Hu, Teng; Liu, Xiaolei; Chen, Ting; Zhang, Xiaosong; Huang, Xiaoming; Niu, Weina; Lu, Jiazhong; Zhou, Kun; Liu, Yuan
【Source】INFORMATION PROCESSING & MANAGEMENT
【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.
【Keywords】Blockchain; Ethereum; Smart contract; Classification; Security
【摘要】区块链技术为各行各业带来创新。以太坊是目前市值第二的区块链平台，也是全球最大的智能合约区块链平台。智能合约可以简化和加速各种应用程序的开发，但也带来了一些问题。例如，智能合约被用来实施欺诈，漏洞合约被故意开发来破坏公平，还有许多重复的合约浪费了性能，没有实际目的。在本文中，我们提出了一种基于交易的以太坊智能合约分类和检测方法来解决这些问题。我们从以太坊收集了超过10000个智能合约，专注于智能合约和用户产生的数据行为。通过手工分析，我们确定了四种交易行为模式，可以用来区分不同类型的合同。在此基础上构建了智能合约的14个基本特征。为了构建实验数据集，我们提出了一种数据切片算法来对收集到的智能合约进行切片。之后，我们使用LSTM网络来训练和测试我们的数据集。大量的实验结果表明，我们的方法可以区分不同类型的契约，并可用于异常检测和恶意契约识别，具有令人满意的准确率、召回率和f1分数。
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