Vulnerability Detection in EOSIO Smart Contracts Based on Teacher-Student Networks

Abstract

The widespread adoption of EOSIO blockchain technology has underscored the critical importance of securing EOSIO smart contracts. Vulnerabilities within these contracts can result in substantial economic losses, making their detection a vital area of research. However, existing methods for detecting vulnerabilities in EOSIO smart contracts predominantly rely on expert-defined rules, which are often susceptible to errors and lack scalability. To address this issue, we propose a vulnerability detection method for the EOSIO platform based on a teacher-student network architecture. This approach specifically focuses on detecting vulnerabilities at the function level. This approach consists of two networks. The teacher network learns both the syntax and semantics of source code and bytecode, while the student network takes bytecode as input. The teacher-student network extracts function-level features by incorporating an MCOAttention mechanism. The student network infers missing bytecode embeddings by learning from the teacher network. By combining the inferred source code and bytecode representations, the method achieves improved accuracy in vulnerability detection. We introduce a cross-modal mutual learning strategy to facilitate knowledge transfer between the teacher and student networks. We evaluated our proposed approach using a dataset comprising smart contracts from the EOSIO platform. Experimental results demonstrate that our method significantly improves accuracy in vulnerability detection.