Ethereum fraud smart contract detection using heterogeneous semantic graph

Abstract

With the rapid development of blockchain technology, various types of fraud is becoming increasingly rampant. Many smart contract-based detection methods have been proposed for typical frauds, such as Ponzi scheme, honeypot and phishing. However, these methods are often lack of the extraction and application of the deep semantics of smart contract or are customized for specific fraud, resulting in limited performance and universality. In this paper, we propose a Ethereum fraud smart contract detection method based on Heterogeneous Semantic Graph(HSG) and Heterogeneous Graph Neural Network(HGNN), which extracts the high-level semantics of smart contracts and designs a graph classifier based on Heterogeneous Graph Transformer(HGT) model to detect fraud smart contracts. Experiments on Ponzi scheme, honeypot and phishing smart contract datasets demonstrate that our method is capable of extracting smart contract semantics more effectively and is superior to or equal to various existing fraud smart contract detection methods, and has universality in fraud smart contract detection tasks.