An Automated Smart Contract Vulnerability Detection Framework Using Static Analysis and Graph Neural Networks

International Journal of Computer Science and Technology | Vol. 2, No. 8, August 2021 | pp. 57–64

Research Article

Title: An Automated Smart Contract Vulnerability Detection Framework Using Static Analysis and Graph Neural Networks

Names of Authors: Yuki Nakamura¹, Aiko Tanaka², and Daiki Sato³

Authors’ Affiliations:
¹Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan
²Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
³Department of Information Engineering, Nagoya University, Nagoya, Japan

Abstract: Smart contracts deployed on public blockchain platforms manage billions of dollars in digital assets, making them high-stakes targets for cyber exploits. Structural vulnerabilities such as reentrancy, integer overflows, and timestamp dependencies have historically led to catastrophic capital losses. Traditional vulnerability identification relies on manual auditing or rule-based static symbolic execution, both of which struggle with high false-positive rates and fail to uncover deep semantic bugs in complex contract control flows. This paper proposes a deep-learning-driven smart contract vulnerability detection framework that combines static analysis with Graph Neural Networks (GNNs). The system compiles raw Solidity smart contract source code into abstract syntax trees (ASTs) and control flow graphs (CFGs), which are then merged into a unified Code Property Graph (CPG) representing data dependencies and execution paths. A Bidirectional Gated Graph Neural Network (BGGNN) processes these graphs to learn vector embeddings of the contract's structural features. Experimental evaluations on a curated dataset of 45,000 smart contracts demonstrate that the proposed framework identifies critical vulnerabilities with an overall accuracy of 96.4% and an F1-score of 0.958, outperforming conventional tools like Oyente, Mythril, and standard recurrent neural networks. The system provides an efficient, automated security validation utility for smart contract deployment pipelines.

Keywords: Smart contract security, Blockchain, Graph neural networks, Static analysis, Vulnerability detection, Deep learning

Manuscript Timeline: Received: May 12, 2021; Revised: June 19, 2021; Accepted: July 15, 2021; Published: August 1, 2021

Citation: Nakamura, Y., Tanaka, A., & Sato, D. (2021). An automated smart contract vulnerability detection framework using static analysis and graph neural networks. International Journal of Computer Science and Technology, 2(8), 57–64. DOI: 10.46882/2021/IJCST/000020

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