Article
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Smart Contract Vulnerability Detection based on Multi-scale Encoders
Version 1
: Received: 29 December 2023 / Approved: 29 December 2023 / Online: 3 January 2024 (08:25:24 CET)
A peer-reviewed article of this Preprint also exists.
Guo, J.; Lu, L.; Li, J. Smart Contract Vulnerability Detection Based on Multi-Scale Encoders. Electronics 2024, 13, 489. Guo, J.; Lu, L.; Li, J. Smart Contract Vulnerability Detection Based on Multi-Scale Encoders. Electronics 2024, 13, 489.
Abstract
Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, by using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-scale Encoder Vulnerability Detection (MEVD) approach to detect well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model's attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%.
Keywords
Smart contract; deep learning; Multi-scale; vulnerability detection
Subject
Computer Science and Mathematics, Software
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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