A Vulnerability-Type Correlation-Aware Smart Contract Multivulnerability Detection Model

Abstract

Blockchain technology has been widely used in the field of Internet of Things, providing effective support for solving security challenges in Internet of Things systems. However, due to the immature development language and deployment platform, smart contracts are prone to various vulnerabilities. Considering the immutability of smart contracts, efficient vulnerability detection before deployment is particularly critical. The existing detection methods have two main limitations: they can only identify a limited number of specific vulnerabilities, resulting in low coverage; the implicit correlation information between vulnerability types is ignored. In order to solve these problems, this paper proposes a smart contract multi-vulnerability detection model CorrelaScan (correlation-aware smart contract analyzer) that integrates vulnerability type correlation awareness. The model is based on a multi-task learning architecture, including a shared layer and a specific task layer. The shared layer uses BERT to extract shared features, while the specific task layer uses BiGRU to learn specific task features for vulnerability detection and type classification. In addition, a vulnerability type embedding module is integrated in the task-specific layer. The module mines potential associations by calculating the similarity between smart contract opcodes and vulnerability types, thereby enhancing detection guidance and improving model performance. Experimental verification on public datasets shows that the model can simultaneously detect 10 types of vulnerabilities such as integer overflow or underflow, reentrancy and timestamp dependence, with an average F1 value of 85.22%. Its detection performance exceeds the current state-of-the-art methods.