  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 classifiers based on features extracted from DeFi projects'
tokens' smart contract code (specifically, opcode-based features). Our final
model is a random forest model that achieves an 80\% F-1 score against a
baseline of 50\%. Notably, we further explore the code-based features that are
most important to our model's performance in more detail, analyzing tokens'
Solidity code and conducting cosine similarity analyses. We find that one
element of the code our opcode-based features may be capturing is the
implementation of the SafeMath library, though this does not account for the
entirety of our features. Another contribution of our study is a new data set,
comprised of (a) a verified ground truth data set 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.
