Ponzi Scheme Identification of Smart Contract Based on Multi Feature Fusion.

Due to the anonymity and imperfect supervision of the blockchain, criminal acts committed by criminals on the blockchain are difficult to be investigated. In recent years, scams based on district smart contracts have emerged one after another, among which the losses caused by Ponzi schemes have reached millions of dollars. However, there is little research on smart contract fraud identification at present, and for fraud detection, information utilization is not comprehensive, only based on a single feature or simply fused multiple features directly, without considering the duplication and connection between features. In order to solve these problems, in this paper, we propose a multi feature fusion scheme identification model (MFFSI) for smart contracts. Our contributions mainly include the following two points: 1) In terms of information use, the operands containing the information about the jump relationship of the opcode execution are retained; 2) In feature fusion, the operation code (opcode) and application binary interface (ABI) sequence features are extracted, and attention modules are used to guide the fusion of features to alleviate the interference of irrelevant features on classification. The results show that the model proposed in this paper has a good detection effect. The F1 score is higher than 86%, which is better than the previous.