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.

区块链技术为各个行业带来了创新。以太坊目前是市值第二个区块链平台，也是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，但是它们也会带来一些问题。例如，使用智能合约进行欺诈，故意开发脆弱性合约以破坏公平性，并且有许多重复性合约浪费了性能而没有实际目的。在本文中，我们提出了一种基于交易的以太坊智能合约的分类和检测方法，以解决这些问题。我们从以太坊收集了10,000多个智能合约，并专注于智能合约和用户产生的数据行为。通过手动分析，我们从交易中识别出四种行为模式，可以用来区分不同类型合同之间的差异。然后从中构造出智能合约的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，用于对收集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。广泛的实验结果表明，我们的方法可以区分不同类型的合同，并且可以以令人满意的精度，召回率和f1分数应用于异常检测和恶意合同识别。为了构建实验数据集，我们提出了一种数据切片算法，用于对收集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。广泛的实验结果表明，我们的方法可以区分不同类型的合同，并且可以以令人满意的精度，召回率和f1分数应用于异常检测和恶意合同识别。为了构建实验数据集，我们提出了一种数据切片算法，用于对收集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。广泛的实验结果表明，我们的方法可以区分不同类型的合同，并且可以以令人满意的精度，召回率和f1分数应用于异常检测和恶意合同识别。