Entity Detection in EVM-based Blockchain Networks Using Machine Learning

Abstract

On blockchain platforms, an individual can use multiple wallet accounts to participate in transactions without disclosing identity. This poses great difficulty for entity behavior analytics on blockchain networks. To detect wallets belonging to the same owner, most solutions today rely on either off-chain data such as those on public forums and social media, or heuristic rules that pertain only to the specific blockchain network used. Their scalability and integrability are limited, especially upon changes in the underlying data structure or blockchain mechanisms. We propose to build a machine learning based solution that learns on on-chain transaction data, rather than relying only on heuristic rules. Specifically, we use heuristic methods to collect and label training data, and then apply our proposed machine learning technique to train the model. We focus on EVM blockchain networks and evaluated the proposed approach on two chains: Ethereum and BNB Chain, obtaining a dataset with over 3 million labeled wallet addresses. The detection accuracy can reach more than 90%. This is better than an existing commercial entity detection system which offers only 75%. Our prediction also is around two times better in terms of F-measure.