CodeNet: Code-Targeted Convolutional Neural Network Architecture for Smart Contract Vulnerability Detection
A smart contract is a computer program which is automatically executed with some conditional statements such as “if/then”. Since smart contracts can include some vulnerable program codes, smart contract exploit was recently highlighted as one of the severe threats to Ethereum b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9740682/ |
| _version_ | 1819159005297115136 |
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| author | Seon-Jin Hwang Seok-Hwan Choi Jinmyeong Shin Yoon-Ho Choi |
| author_facet | Seon-Jin Hwang Seok-Hwan Choi Jinmyeong Shin Yoon-Ho Choi |
| author_sort | Seon-Jin Hwang |
| collection | DOAJ |
| description | A smart contract is a computer program which is automatically executed with some conditional statements such as “if/then”. Since smart contracts can include some vulnerable program codes, smart contract exploit was recently highlighted as one of the severe threats to Ethereum blockchain. As one of the efficient and effective smart contract vulnerability detection methods, deep learning methods have been studied due to the fast detection speed and the high detection accuracy. Recently, the deep learning methods using convolutional neural network(CNN) have actively studied to classify images transformed from smart contracts into vulnerable or invulnerable. However, while simply transforming a smart contract into an image and analyzing, semantics and context of the smart contract are ignored to cause false detection alarms. To detect vulnerable smart contracts while maintaining their semantics and context, we propose a new code-targeted CNN architecture, called CodeNet. To improve the performance of CodeNet, we also design a data pre-processing procedure, where a smart contract is transformed into an image while maintaining locality. From the experimental results under various types of vulnerabilities, the proposed CodeNet-based vulnerability detection method shows the good-enough detection performance and detection time compared to well-known state-of-the-art vulnerability detection tools. |
| first_indexed | 2024-12-22T16:33:40Z |
| format | Article |
| id | doaj.art-bec2252ec8d14a21a5eebde55d8408f1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T16:33:40Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bec2252ec8d14a21a5eebde55d8408f12022-12-21T18:20:00ZengIEEEIEEE Access2169-35362022-01-0110325953260710.1109/ACCESS.2022.31620659740682CodeNet: Code-Targeted Convolutional Neural Network Architecture for Smart Contract Vulnerability DetectionSeon-Jin Hwang0Seok-Hwan Choi1https://orcid.org/0000-0003-3590-6024Jinmyeong Shin2Yoon-Ho Choi3https://orcid.org/0000-0002-3556-5082School of Computer Science and Engineering, Pusan National University, Busan, Republic of KoreaSchool of Computer Science and Engineering, Pusan National University, Busan, Republic of KoreaSchool of Computer Science and Engineering, Pusan National University, Busan, Republic of KoreaSchool of Computer Science and Engineering, Pusan National University, Busan, Republic of KoreaA smart contract is a computer program which is automatically executed with some conditional statements such as “if/then”. Since smart contracts can include some vulnerable program codes, smart contract exploit was recently highlighted as one of the severe threats to Ethereum blockchain. As one of the efficient and effective smart contract vulnerability detection methods, deep learning methods have been studied due to the fast detection speed and the high detection accuracy. Recently, the deep learning methods using convolutional neural network(CNN) have actively studied to classify images transformed from smart contracts into vulnerable or invulnerable. However, while simply transforming a smart contract into an image and analyzing, semantics and context of the smart contract are ignored to cause false detection alarms. To detect vulnerable smart contracts while maintaining their semantics and context, we propose a new code-targeted CNN architecture, called CodeNet. To improve the performance of CodeNet, we also design a data pre-processing procedure, where a smart contract is transformed into an image while maintaining locality. From the experimental results under various types of vulnerabilities, the proposed CodeNet-based vulnerability detection method shows the good-enough detection performance and detection time compared to well-known state-of-the-art vulnerability detection tools.https://ieeexplore.ieee.org/document/9740682/Blockchainconvolutional neural networkdeep learningEthereumsmart contractvulnerability detection |
| spellingShingle | Seon-Jin Hwang Seok-Hwan Choi Jinmyeong Shin Yoon-Ho Choi CodeNet: Code-Targeted Convolutional Neural Network Architecture for Smart Contract Vulnerability Detection IEEE Access Blockchain convolutional neural network deep learning Ethereum smart contract vulnerability detection |
| title | CodeNet: Code-Targeted Convolutional Neural Network Architecture for Smart Contract Vulnerability Detection |
| title_full | CodeNet: Code-Targeted Convolutional Neural Network Architecture for Smart Contract Vulnerability Detection |
| title_fullStr | CodeNet: Code-Targeted Convolutional Neural Network Architecture for Smart Contract Vulnerability Detection |
| title_full_unstemmed | CodeNet: Code-Targeted Convolutional Neural Network Architecture for Smart Contract Vulnerability Detection |
| title_short | CodeNet: Code-Targeted Convolutional Neural Network Architecture for Smart Contract Vulnerability Detection |
| title_sort | codenet code targeted convolutional neural network architecture for smart contract vulnerability detection |
| topic | Blockchain convolutional neural network deep learning Ethereum smart contract vulnerability detection |
| url | https://ieeexplore.ieee.org/document/9740682/ |
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