【Author】
Fu, Qishuang; Lin, Dan; Wu, Jiajing; Zheng, Zibin
【Source】IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
【Abstract】As the largest blockchain platform that supports smart contracts, Ethereum has attracted wide attention from both academia and industry in recent years. Along with the prosperous development of Ethereum, the high-risk illegal practices on it are becoming more and more rampant, seriously jeopardizing the system's trading security and long-term development. Therefore, the detection and quantification of account risk are of great importance for both cryptocurrency investors and blockchain security researchers. In this article, we propose the first general framework for account risk rating on Ethereum, which includes a devisable suspiciousness metric to adapt to various illicit fraud detection and a network propagation mechanism to formulate the relations between accounts and transactions. By conducting extensive experiments on a real-world dataset from Ethereum, we show the universality of the account risk rating framework. Particularly, statistical analyses on different risk levels of accounts demonstrate that the risk rating framework has access to detect various illicit accounts. And the metric analysis of risk rating results put forward some insights. Moreover, visualization of a suspicious transaction chain reveals the process of illicit activities on Ethereum, enabling investors to obtain an understanding of the risky accounts and avoid significant financial losses.
【Keywords】Blockchains; Measurement; Fraud; Cryptocurrency; Task analysis; Feature extraction; Biological system modeling; Blockchain; Ethereum; general framework; network propagation; risk rating
【文献类型】Article; Early Access
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