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G-Scan: Graph Neural Networks for Line-Level Vulnerability Identification in Smart Contracts
- Publication Year :
- 2023
-
Abstract
- Due to the immutable and decentralized nature of Ethereum (ETH) platform, smart contracts are prone to security risks that can result in financial loss. While existing machine learning-based vulnerability detection algorithms achieve high accuracy at the contract level, they require developers to manually inspect source code to locate bugs. To this end, we present G-Scan, the first end-to-end fine-grained line-level vulnerability detection system evaluated on the first-of-its-kind real world dataset. G-Scan first converts smart contracts to code graphs in a dependency and hierarchy preserving manner. Next, we train a graph neural network to identify vulnerable nodes and assess security risks. Finally, the code graphs with node vulnerability predictions are mapped back to the smart contracts for line-level localization. We train and evaluate G-Scan on a collected real world smart contracts dataset with line-level annotations on reentrancy vulnerability, one of the most common and severe types of smart contract vulnerabilities. With the well-designed graph representation and high-quality dataset, G-Scan achieves 93.02% F1-score in contract-level vulnerability detection and 93.69% F1-score in line-level vulnerability localization. Additionally, the lightweight graph neural network enables G-Scan to localize vulnerabilities in 6.1k lines of code smart contract within 1.2 seconds.
- Subjects :
- Computer Science - Cryptography and Security
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2307.08549
- Document Type :
- Working Paper