Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy Vulnerability
Blockchain technology is currently evolving rapidly, and smart contracts are the hallmark of the second generation of blockchains. Currently, smart contracts are gradually being used in power system networks to build a decentralized energy system. Security is very important to power systems and atta...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/24/9642 |
| _version_ | 1797459428587339776 |
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| author | Ran Guo Weijie Chen Lejun Zhang Guopeng Wang Huiling Chen |
| author_facet | Ran Guo Weijie Chen Lejun Zhang Guopeng Wang Huiling Chen |
| author_sort | Ran Guo |
| collection | DOAJ |
| description | Blockchain technology is currently evolving rapidly, and smart contracts are the hallmark of the second generation of blockchains. Currently, smart contracts are gradually being used in power system networks to build a decentralized energy system. Security is very important to power systems and attacks launched against smart contract vulnerabilities occur frequently, seriously affecting the development of the smart contract ecosystem. Current smart contract vulnerability detection tools suffer from low correct rates and high false positive rates, which cannot meet current needs. Therefore, we propose a smart contract vulnerability detection system based on the Siamese network in this paper. We improved the original Siamese network model to perform smart contract vulnerability detection by comparing the similarity of two sub networks with the same structure and shared parameters. We also demonstrate, through extensive experiments, that the model has better vulnerability detection performance and lower false alarm rate compared with previous research results. |
| first_indexed | 2024-03-09T16:51:12Z |
| format | Article |
| id | doaj.art-c7ded3a6c51b4ab1a48d46e2412dc3d6 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T16:51:12Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c7ded3a6c51b4ab1a48d46e2412dc3d62023-11-24T14:40:55ZengMDPI AGEnergies1996-10732022-12-011524964210.3390/en15249642Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy VulnerabilityRan Guo0Weijie Chen1Lejun Zhang2Guopeng Wang3Huiling Chen4School of Physics and Materials Science, Guangzhou University, Guangzhou 510006, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou 225127, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou 225127, ChinaEngineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Beijing 100039, ChinaDepartment of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaBlockchain technology is currently evolving rapidly, and smart contracts are the hallmark of the second generation of blockchains. Currently, smart contracts are gradually being used in power system networks to build a decentralized energy system. Security is very important to power systems and attacks launched against smart contract vulnerabilities occur frequently, seriously affecting the development of the smart contract ecosystem. Current smart contract vulnerability detection tools suffer from low correct rates and high false positive rates, which cannot meet current needs. Therefore, we propose a smart contract vulnerability detection system based on the Siamese network in this paper. We improved the original Siamese network model to perform smart contract vulnerability detection by comparing the similarity of two sub networks with the same structure and shared parameters. We also demonstrate, through extensive experiments, that the model has better vulnerability detection performance and lower false alarm rate compared with previous research results.https://www.mdpi.com/1996-1073/15/24/9642smart contractdeep learningsiamese network |
| spellingShingle | Ran Guo Weijie Chen Lejun Zhang Guopeng Wang Huiling Chen Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy Vulnerability Energies smart contract deep learning siamese network |
| title | Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy Vulnerability |
| title_full | Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy Vulnerability |
| title_fullStr | Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy Vulnerability |
| title_full_unstemmed | Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy Vulnerability |
| title_short | Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy Vulnerability |
| title_sort | smart contract vulnerability detection model based on siamese network scvsn a case study of reentrancy vulnerability |
| topic | smart contract deep learning siamese network |
| url | https://www.mdpi.com/1996-1073/15/24/9642 |
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