Multi-Modal Semantic Fusion for Smart Contract Vulnerability Detection in Cloud-Based Blockchain Analytics Platforms.
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| Title: | Multi-Modal Semantic Fusion for Smart Contract Vulnerability Detection in Cloud-Based Blockchain Analytics Platforms. |
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| Authors: | Zeng, Xingyu, Wen, Qiaoyan, Qin, Sujuan |
| Source: | Electronics (2079-9292); Nov2025, Vol. 14 Issue 21, p4188, 22p |
| Subject Terms: | CLOUD computing, BLOCKCHAINS, MACHINE learning, PENETRATION testing (Computer security), DATA security, MULTISENSOR data fusion |
| Abstract: | With the growth of trusted computing demand for big data analysis, cloud computing platforms are reshaping trusted data infrastructure by integrating Blockchain as a Service (BaaS), which uses elastic resource scheduling and heterogeneous hardware acceleration to support petabyte level multi-institution data security exchange in medical, financial, and other fields. As the core hub of data-intensive scenarios, the BaaS platform has the dual capabilities of privacy computing and process automation. However, its deep dependence on smart contracts generates new code layer vulnerabilities, resulting in malicious contamination of analysis results. The existing detection schemes are limited to the perspective of single-source data, which makes it difficult to capture both global semantic associations and local structural details in a cloud computing environment, leading to a performance bottleneck in terms of scalability and detection accuracy. To address these challenges, this paper proposes a smart contract vulnerability detection method based on multi-modal semantic fusion for the blockchain analysis platform of cloud computing. Firstly, the contract source code is parsed into an abstract syntax tree, and the key code is accurately located based on the predefined vulnerability feature set. Then, the text features and graph structure features of key codes are extracted in parallel to realize the deep fusion of them. Finally, with the help of attention enhancement, the vulnerability probability is output through the fully connected network. The experiments on Ethereum benchmark datasets show that the detection accuracy of our method for re-entrancy vulnerability, timestamp vulnerability, overflow/underflow vulnerability, and delegatecall vulnerability can reach 92.2%, 96.3%, 91.4%, and 89.5%, surpassing previous methods. Additionally, our method has the potential for practical deployment in cloud-based blockchain service environments. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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