Ponzi Scheme Detection Based on CNN and BiGRU combined with Attention Mechanism

Abstract

The anonymity of blockchain and its inadequate supervision make it difficult to investigate criminal activities on the blockchain. In recent years, criminals have profited from deploying Ponzi schemes on the Ethereum blockchain through smart contracts, resulting in substantial economic losses and adverse impacts, seriously impeding the development of the blockchain community and technology. However, despite some research on identifying Ponzi schemes on Ethereum, existing methods face certain difficulties in data acquisition, complex feature construction, and insufficient exploration of opcode data features. To address these issues, this paper proposes a method that only relies on smart contract opcodes to verify whether a contract is a Ponzi scheme. Specifically, Word2vec word embedding technology is first used to train the data, obtaining opcode word vector representations through the training process. Subsequently, by passing the word vectors into the Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit Network (BiGRU) models, spatial and semantic features are extracted to better capture semantic information at different levels of smart contract opcodes. The attention mechanism allocates different weights to various features, highlighting key attributes. The results of the experiment show that the proposed method displays a strong detection performance, indicating specific improvements in precision, recall, and F1 score in contrast to previous methods.