Early Detection of Smart Ponzi Scheme Contracts Based on Behavior Forest Similarity

Smart contracts empowered by blockchains often manage digital assets in a distributed and decentralized environment. People believe in smart contracts based on these new technologies. Unfortunately, malicious smart contacts, such as smart Ponzi scheme contracts (ponzitracts, for short), pose risk. Existing techniques detect ponzitracts by analyzing the code as well as a large amount of transaction data after time-consuming deployment. However, a conclusion based on transaction data can only be gotten after the damage has been caused. This paper proposes PonziDetector, a ponzitract detection technique that does not rely on transaction data. Behavior forest is introduced into PonziDetector to capture dynamic behaviors of smart contracts during interacting with them, which makes it possible to early detect ponzitracts. The empirical study demonstrates that PonziDetector, without transaction data, can improve the precision and the recall of the state-of-The-Art to 94.6% and 93.0% respectively. This means that PonziDetector can avoid potential losses by early detecting ponzitracts.