RiskProp: Account Risk Rating on Ethereum via De-anonymous Score and Network Propagation

Dan Lin, Jiajing Wu, Qishuang Fu, Zibin Zheng, Ting Chen

Abstract: As one of the most popular blockchain platforms supporting smart contracts, Ethereum has caught the interest of both investors and criminals. Differently from traditional financial scenarios, executing Know Your Customer verification on Ethereum is rather difficult due to its pseudonymous nature. Fortunately, as the transaction records stored in the Ethereum blockchain are publicly accessible, we can understand the behavior of accounts or detect illicit activities via transaction mining. Existing risk control techniques have primarily been developed from the perspectives of de-anonymizing address clustering and illicit account classification. However, these techniques cannot ascertain the potential risks for all accounts and are limited by specific heuristic strategies or insufficient label information. These constraints motivate us to seek an effective rating method for quantifying the spread of risk in a transaction network. To the best of our knowledge, we are the first to address the problem of account risk rating on Ethereum by proposing a novel model called RiskProp, which includes a de-anonymous score to measure transaction anonymity and a network propagation mechanism to formulate the relationships between accounts and transactions. Experimental results on a realistic Ethereum dataset demonstrate that proposed RiskProp newly discovered 63% of the Top 150 high-risk accounts as suspicious. The superior performance of risk score-based account classification experiments further verifies the effectiveness of our rating method (85.63% accuracy).

External IDs:dblp:journals/tdsc/LinWFZC25