Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In the past year, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, in particular, various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges to governments trying to mitigate potential offending in this space. This study aims to uncover 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 adapt prior work on detecting specific types of securities violations across Ethereum, building a random forest classifier based on features extracted from DeFi projects' tokens' smart contract code. The final classifier achieves a 98.6% F1-score. From further feature-level analysis, we find a single feature makes this a highly detectable problem. The high reliance on a single feature means that, at this stage, a complex machine learning model may not be necessary or desirable for this problem. However, this may change as DeFi securities violations become more sophisticated. Another contribution of our study is a new dataset, comprised of (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 the wider legal context.
翻译:分散金融(DeFi) 是一个金融产品和服务体系,它通过各种供应链的智能合同建立和提供。在过去一年中, DeFi 获得了受欢迎程度和市场资本化,但是,它也与犯罪有关,特别是各种类型的证券违规。 DeFi 缺乏了解客户的要求,对试图减少这一空间潜在犯罪的政府提出了挑战。本研究旨在发现这一问题是否适合机器学习方法,即我们是否能够发现DeFi项目可能根据象征物的智能合同代码参与证券违规。我们在Etheum 发现特定类型的证券违规项目之前,已经适应了工作,根据从 DeFi 项目标志的智能合同代码中提取的特征建立一个随机的森林分类器。在DeFi 项目的智能合同代码中,最后的分类器达到了98.6%的F1核心。从进一步的地段分析中,我们发现一个单一的特征使这一问题非常容易被察觉。高度依赖一个单一的特征模型意味着,在这个阶段,一个复杂的机器学习模式也许不必要或不适宜。然而,这可能会随着DeFi 合法证券违规的背景而改变,而成为更复杂的法律背景,从一个真实的真相研究。