SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection
With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people’s lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/12/4621 |
| _version_ | 1797482339745398784 |
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| author | Lejun Zhang Yuan Li Tianxing Jin Weizheng Wang Zilong Jin Chunhui Zhao Zhennao Cai Huiling Chen |
| author_facet | Lejun Zhang Yuan Li Tianxing Jin Weizheng Wang Zilong Jin Chunhui Zhao Zhennao Cai Huiling Chen |
| author_sort | Lejun Zhang |
| collection | DOAJ |
| description | With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people’s lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection. |
| first_indexed | 2024-03-09T22:30:53Z |
| format | Article |
| id | doaj.art-674a4b27177045848022dc9387fb8a5f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:30:53Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-674a4b27177045848022dc9387fb8a5f2023-11-23T18:56:18ZengMDPI AGSensors1424-82202022-06-012212462110.3390/s22124621SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability DetectionLejun Zhang0Yuan Li1Tianxing Jin2Weizheng Wang3Zilong Jin4Chunhui Zhao5Zhennao Cai6Huiling Chen7College of Information Engineering, Yangzhou University, Yangzhou 225127, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou 225127, ChinaYangzhou Marine Electronic Instrument Research Institute, Yangzhou 225001, ChinaComputer Science Department, City University of Hong Kong, Hong KongSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210004, ChinaCollege of Information and Communication Engineering, 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, ChinaWith countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people’s lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection.https://www.mdpi.com/1424-8220/22/12/4621blockchainIoTsmart contractvulnerability detectiondeep learningserial hybrid network |
| spellingShingle | Lejun Zhang Yuan Li Tianxing Jin Weizheng Wang Zilong Jin Chunhui Zhao Zhennao Cai Huiling Chen SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection Sensors blockchain IoT smart contract vulnerability detection deep learning serial hybrid network |
| title | SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection |
| title_full | SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection |
| title_fullStr | SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection |
| title_full_unstemmed | SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection |
| title_short | SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection |
| title_sort | spcbig ec a robust serial hybrid model for smart contract vulnerability detection |
| topic | blockchain IoT smart contract vulnerability detection deep learning serial hybrid network |
| url | https://www.mdpi.com/1424-8220/22/12/4621 |
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