ESA: An Enhanced Sequential Algorithm-Based Model for Smart Contract Vulnerability Detection

Abstract

The current security problem of smart contracts is becoming a common concern for researchers and developers. Existing smart contract vulnerability detection methods rely heavily on fixed expert rules, resulting in low detection accuracy. In order to cope with complex and changing smart contract application scenarios, we chose to use graph neural networks to detect vulnerabilities. In this paper, we proposed a vulnerability detection model called ESA based on the enhanced sequential algorithm. During the coding process, the contract function source code is described as a contract graph, which increases the model’s global insight into node features during the learning process and reduces the number of noise nodes unrelated to vulnerabilities while retaining sufficient contextual semantic features. Compared to the cutting-edge methods, our model has significantly improved the accuracy of reentrant and timestamp dependency vulnerabilities, with detection accuracies of 89.09% and 88.49%, respectively.