CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model
In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has recei...
| Main Authors: | , , , , , , |
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/9/3577 |
| _version_ | 1797502690613264384 |
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| author | Lejun Zhang Weijie Chen Weizheng Wang Zilong Jin Chunhui Zhao Zhennao Cai Huiling Chen |
| author_facet | Lejun Zhang Weijie Chen Weizheng Wang Zilong Jin Chunhui Zhao Zhennao Cai Huiling Chen |
| author_sort | Lejun Zhang |
| collection | DOAJ |
| description | In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance. |
| first_indexed | 2024-03-10T03:39:41Z |
| format | Article |
| id | doaj.art-040ca2e2762c4fed9f705272910ece1d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:39:41Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-040ca2e2762c4fed9f705272910ece1d2023-11-23T09:20:27ZengMDPI AGSensors1424-82202022-05-01229357710.3390/s22093577CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid ModelLejun Zhang0Weijie Chen1Weizheng Wang2Zilong Jin3Chunhui Zhao4Zhennao Cai5Huiling Chen6College of Information Engineering, Yangzhou University, Yangzhou 225127, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou 225127, ChinaComputer Science Department, City University of Hong Kong, Hong KongSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin 150001, ChinaDepartment of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaDepartment of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaIn the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance.https://www.mdpi.com/1424-8220/22/9/3577smart contractsecurityvulnerability detectionhybrid model |
| spellingShingle | Lejun Zhang Weijie Chen Weizheng Wang Zilong Jin Chunhui Zhao Zhennao Cai Huiling Chen CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model Sensors smart contract security vulnerability detection hybrid model |
| title | CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model |
| title_full | CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model |
| title_fullStr | CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model |
| title_full_unstemmed | CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model |
| title_short | CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model |
| title_sort | cbgru a detection method of smart contract vulnerability based on a hybrid model |
| topic | smart contract security vulnerability detection hybrid model |
| url | https://www.mdpi.com/1424-8220/22/9/3577 |
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