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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,...

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Autores principales: Deng, Weichu, Wei, Huanchun, Huang, Teng, Cao, Cong, Peng, Yun, Hu, Xuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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