Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models
In the last decade, smart contract security issues lead to tremendous losses, which has attracted increasing public attention both in industry and in academia. Researchers have embarked on efforts with logic rules, symbolic analysis, and formal analysis to achieve encouraging results in smart contra...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8970384/ |
| _version_ | 1818665182953472000 |
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| author | Peng Qian Zhenguang Liu Qinming He Roger Zimmermann Xun Wang |
| author_facet | Peng Qian Zhenguang Liu Qinming He Roger Zimmermann Xun Wang |
| author_sort | Peng Qian |
| collection | DOAJ |
| description | In the last decade, smart contract security issues lead to tremendous losses, which has attracted increasing public attention both in industry and in academia. Researchers have embarked on efforts with logic rules, symbolic analysis, and formal analysis to achieve encouraging results in smart contract vulnerability detection tasks. However, the existing detection tools are far from satisfactory. In this paper, we attempt to utilize the deep learning-based approach, namely bidirectional long-short term memory with attention mechanism (BLSTM-ATT), aiming to precisely detect reentrancy bugs. Furthermore, we propose contract snippet representations for smart contracts, which contributes to capturing essential semantic information and control flow dependencies. Our extensive experimental studies on over 42,000 real-world smart contracts show that our proposed model and contract snippet representations significantly outperform state-of-the-art methods. In addition, this work proves that it is practical to apply deep learning-based technology on smart contract vulnerability detection, which is able to promote future research towards this area. |
| first_indexed | 2024-12-17T05:44:35Z |
| format | Article |
| id | doaj.art-3d01fb8850fe448d9c2dc9edd0a9afe6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:44:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3d01fb8850fe448d9c2dc9edd0a9afe62022-12-21T22:01:21ZengIEEEIEEE Access2169-35362020-01-018196851969510.1109/ACCESS.2020.29694298970384Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential ModelsPeng Qian0https://orcid.org/0000-0003-4934-5811Zhenguang Liu1Qinming He2Roger Zimmermann3Xun Wang4School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaSchool of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaDepartment of Computer Science, Zhejiang University, Hangzhou, ChinaSchool of Computing, National University of Singapore, SingaporeSchool of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, ChinaIn the last decade, smart contract security issues lead to tremendous losses, which has attracted increasing public attention both in industry and in academia. Researchers have embarked on efforts with logic rules, symbolic analysis, and formal analysis to achieve encouraging results in smart contract vulnerability detection tasks. However, the existing detection tools are far from satisfactory. In this paper, we attempt to utilize the deep learning-based approach, namely bidirectional long-short term memory with attention mechanism (BLSTM-ATT), aiming to precisely detect reentrancy bugs. Furthermore, we propose contract snippet representations for smart contracts, which contributes to capturing essential semantic information and control flow dependencies. Our extensive experimental studies on over 42,000 real-world smart contracts show that our proposed model and contract snippet representations significantly outperform state-of-the-art methods. In addition, this work proves that it is practical to apply deep learning-based technology on smart contract vulnerability detection, which is able to promote future research towards this area.https://ieeexplore.ieee.org/document/8970384/Blockchainsmart contractdeep learningsequential modelsvulnerability detection |
| spellingShingle | Peng Qian Zhenguang Liu Qinming He Roger Zimmermann Xun Wang Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models IEEE Access Blockchain smart contract deep learning sequential models vulnerability detection |
| title | Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models |
| title_full | Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models |
| title_fullStr | Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models |
| title_full_unstemmed | Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models |
| title_short | Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models |
| title_sort | towards automated reentrancy detection for smart contracts based on sequential models |
| topic | Blockchain smart contract deep learning sequential models vulnerability detection |
| url | https://ieeexplore.ieee.org/document/8970384/ |
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