CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model

• Main Authors: Cai, Z. (Author), Chen, H. (Author), Chen, W. (Author), Jin, Z. (Author), Wang, W. (Author), Zhang, L. (Author), Zhao, C. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Analysis tools; Block-chain; Detection methods; Detection performance; Financial loss; hybrid model; Hybrid model; Learning models; Long short-term memory; Losses; security; Security; Security vulnerabilities; smart contract; Smart contract; vulnerability detection; Vulnerability detection
• Online Access: View Fulltext in Publisher

 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.