Paper Info
Title
CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model
Abstract
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.
Year
DOI
Venue
2022
10.3390/s22093577
SENSORS
Keywords
DocType
Volume
smart contract, security, vulnerability detection, hybrid model
Journal
22
Issue
ISSN
Citations 
9
1424-8220
0
PageRank 
References 
Authors
0.34
2
7
Name
Order
Citations
PageRank
Lejun Zhang17815.62
Weijie Chen200.34
Weizheng Wang301.35
Zilong Jin4298.32
Chunhui Zhao59726.94
Zhennao Cai622.37
Huiling Chen740228.49