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A Vulnerability-Type Correlation-Aware Smart Contract Multivulnerability Detection Model
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DOI：10.1109/JIOT.2026.3677538.png)
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Abstract
En 中文
Blockchain technology has been widely used in the field of Internet of Things (IoT), providing effective support for solving security challenges in IoT systems. However, due to the immature development of the 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. To solve these problems, this article proposes a smart contract multivulnerability detection model, a correlation-aware smart contract analyzer (CorrelaScan) that integrates vulnerability-type correlation awareness. The model is based on a multitask 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 bidirectional gated recurrent units (BiGRUs) to learn specific task features for vulnerability detection and type classification. In addition, a vulnerability-type embedding (VTE) 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 ten 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.
Keywords:
Multitask learning
multivulnerability detection
smart contracts
vulnerability-type embedding (VTE)
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