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With the development of blockchain technology, more and more attention hasbeen paid to the intersection of blockchain and education, and variouseducational evaluation systems and E-learning systems are developed based onblockchain technology. Among them, Ethereum smart contract is favored bydevelopers for its ``event-triggered" mechanism for building educationintelligent trading systems and intelligent learning platforms. However, due tothe immutability of blockchain, published smart contracts cannot be modified,so problematic contracts cannot be fixed by modifying the code in theeducational blockchain. In recent years, security incidents due to smartcontract vulnerabilities have caused huge property losses, so the detection ofsmart contract vulnerabilities in educational blockchain has become a greatchallenge. To solve this problem, this paper proposes a graph neural network(GNN) based vulnerability detection for smart contracts in educationalblockchains. Firstly, the bytecodes are decompiled to get the opcode. Secondly,the basic blocks are divided, and the edges between the basic blocks accordingto the opcode execution logic are added. Then, the control flow graphs (CFG)are built. Finally, we designed a GNN-based model for vulnerability detection.The experimental results show that the proposed method is effective for thevulnerability detection of smart contracts. Compared with the traditionalapproaches, it can get good results with fewer layers of the GCN model, whichshows that the contract bytecode and GCN model are efficient in vulnerabilitydetection.

Graph Neural Networks Enhanced Smart Contract Vulnerability Detection of Educational Blockchain