Abstract
With smart contracts rapidly proliferating, the efficiency of existing detection methods is inadequate. Detecting loopholes in contracts is a critical concern, and in this article, we present a fragmented, symbolic representation of smart contracts aimed to capture vital vulnerability semantic information and control flow correlation. Furthermore, for in-depth analysis of vulnerabilities in extensive code fragments, we refine the conventional attention mechanism to balance attention weights based on code semantics and context-specific features. We also integrate the text classification model TextRNN with the improved attention mechanism (LinkAttention) to precisely identify reentrancy vulnerabilities. Our experimental studies conducted on diverse real-world smart contracts suggest that our method outperforms existing vulnerability detection tools.
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Xu, H., Qiu, M., Zhao, H. (2024). Reentrancy Vulnerability Detection Based on Improved Attention Mechanism. In: Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., Wang, Y. (eds) Knowledge Science, Engineering and Management. KSEM 2024. Lecture Notes in Computer Science(), vol 14886. Springer, Singapore. https://doi.org/10.1007/978-981-97-5498-4_25
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DOI: https://doi.org/10.1007/978-981-97-5498-4_25
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