Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In recent years, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, particularly various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses. This study aims to determine whether this problem is suited to a machine learning approach, namely, whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’ smart contract code. We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’ tokens’ smart contract code (specifically, opcode-based features). Our final model was a random forest model that achieved an 80% F-1 score against a baseline of 50%. Notably, we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’ Solidity code and conducting cosine similarity analyses. We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library, although this does not account for the entirety of our features. Another contribution of our study is a new dataset, comprising (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of legitimate tokens from a reputable DeFi aggregator. This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.

中文翻译：

---

从代币智能合约代码中检测 DeFi 证券违规行为

去中心化金融（DeFi）是通过各种区块链上的智能合约构建和提供的金融产品和服务系统。近年来，DeFi 得到了普及和市值。然而，它也与犯罪有关，特别是各种类型的证券违规行为。DeFi 缺乏“了解你的客户”要求，给试图减少潜在犯罪行为的政府带来了挑战。本研究旨在确定这个问题是否适合机器学习方法，即我们是否可以根据代币的智能合约代码来识别可能涉及证券违规的 DeFi 项目。我们改编了之前关于检测以太坊上特定类型的证券违规行为的工作，根据从 DeFi 项目代币的智能合约代码中提取的特征（特别是基于操作码的特征）构建分类器。我们的最终模型是随机森林模型，其 F-1 得分为 80%，而基线为 50%。值得注意的是，我们通过分析代币的 Solidity 代码并进行余弦相似度分析，进一步更详细地探索了对我们模型性能最重要的基于代码的功能。我们发现基于操作码的功能可以捕获的代码元素之一是 SafeMath 库的实现，尽管这并不能解释我们的全部功能。我们研究的另一个贡献是一个新的数据集，包括 (a) 涉及证券违规的代币的经过验证的真实数据集和 (b) 来自信誉良好的 DeFi 聚合商的一组合法代币。本文进一步讨论了检察官在执法工作中可能使用像我们这样的模型，并将其与更广泛的法律背景联系起来。