SGDL: Smart contract vulnerability generation via deep learning

Abstract

The growing popularity of smart contracts in various areas, such as digital payments and the Internet of Things, has led to an increase in smart contract security challenges. Researchers have responded by developing vulnerability detection tools. However, the effectiveness of these tools is limited due to the lack of authentic smart contract vulnerability datasets to comprehensively assess their capacity for diverse vulnerabilities. This paper proposes a Deep Learning-based Smart contract vulnerability Generation approach (SGDL) to overcome this challenge. SGDL utilizes static analysis techniques to extract both syntactic and semantic information from the contracts. It then uses a classification technique to match injected vulnerabilities with contracts. A generative adversarial network is employed to generate smart contract vulnerability fragments, creating a diverse and authentic pool of fragments. The vulnerability fragments are then injected into the smart contracts using an abstract syntax tree to ensure their syntactic correctness. Our experimental results demonstrate that our method is more effective than existing vulnerability injection methods in evaluating the contract vulnerability detection capacity of existing detection tools. Overall, SGDL provides a comprehensive and innovative solution to address the critical issue of authentic and diverse smart contract vulnerability datasets.