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Article

SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection

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College of Information Engineering, Yangzhou University, Yangzhou 225127, China
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Research and Development Center for E-Learning, Ministry of Education, Beijing 100039, China
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Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou 510006, China
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Yangzhou Marine Electronic Instrument Research Institute, Yangzhou 225001, China
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Computer Science Department, City University of Hong Kong, Hong Kong
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School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210004, China
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College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
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Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Kamanashis Biswas, Mohammad Jabed Morshed Chowdhury, Muhammad Usman and Naveen Chilamkurti
Sensors 2022, 22(12), 4621; https://doi.org/10.3390/s22124621
Received: 31 March 2022 / Revised: 26 May 2022 / Accepted: 17 June 2022 / Published: 19 June 2022
(This article belongs to the Special Issue Blockchain of Things: Benefits, Challenges and Future Directions)
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. View Full-Text
Keywords: blockchain; IoT; smart contract; vulnerability detection; deep learning; serial hybrid network blockchain; IoT; smart contract; vulnerability detection; deep learning; serial hybrid network
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MDPI and ACS Style

Zhang, L.; Li, Y.; Jin, T.; Wang, W.; Jin, Z.; Zhao, C.; Cai, Z.; Chen, H. SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection. Sensors 2022, 22, 4621. https://doi.org/10.3390/s22124621

AMA Style

Zhang L, Li Y, Jin T, Wang W, Jin Z, Zhao C, Cai Z, Chen H. SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection. Sensors. 2022; 22(12):4621. https://doi.org/10.3390/s22124621

Chicago/Turabian Style

Zhang, Lejun, Yuan Li, Tianxing Jin, Weizheng Wang, Zilong Jin, Chunhui Zhao, Zhennao Cai, and Huiling Chen. 2022. "SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection" Sensors 22, no. 12: 4621. https://doi.org/10.3390/s22124621

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