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Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion
With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459372/ https://www.ncbi.nlm.nih.gov/pubmed/37631785 http://dx.doi.org/10.3390/s23167246 |
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author | Deng, Weichu Wei, Huanchun Huang, Teng Cao, Cong Peng, Yun Hu, Xuan |
author_facet | Deng, Weichu Wei, Huanchun Huang, Teng Cao, Cong Peng, Yun Hu, Xuan |
author_sort | Deng, Weichu |
collection | PubMed |
description | With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities. |
format | Online Article Text |
id | pubmed-10459372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104593722023-08-27 Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion Deng, Weichu Wei, Huanchun Huang, Teng Cao, Cong Peng, Yun Hu, Xuan Sensors (Basel) Article With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities. MDPI 2023-08-18 /pmc/articles/PMC10459372/ /pubmed/37631785 http://dx.doi.org/10.3390/s23167246 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Deng, Weichu Wei, Huanchun Huang, Teng Cao, Cong Peng, Yun Hu, Xuan Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion |
title | Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion |
title_full | Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion |
title_fullStr | Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion |
title_full_unstemmed | Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion |
title_short | Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion |
title_sort | smart contract vulnerability detection based on deep learning and multimodal decision fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459372/ https://www.ncbi.nlm.nih.gov/pubmed/37631785 http://dx.doi.org/10.3390/s23167246 |
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