An Efficient Smart Contract Vulnerability Detector Based on Semantic Contract Graphs Using Approximate Graph Matching
Yingli Zhang, Jiali Ma, Xin Liu, Guodong Ye, Qun Jin, Jianhua Ma, Qingguo Zhou
Abstract
The Internet of Things (IoT) has become a focus of information infrastructure development in recent years. The smart blockchain can provide various solutions for trust, security, and privacy (TSP) challenges to protect IoT data, and smart contracts are the foundation of blockchain intelligence, and greatly enhance the ability of smart blockchain to solve TSP problems. So, the security of smart contracts must be addressed. We propose an efficient smart contract vulnerability detector to improve the safety of smart contracts. It comprises a graph extraction method and a complete vulnerability detection process. The graph extraction method consists of vulnerability pattern extraction and a graph generation process. The vulnerability detection process first uses the approximate graph matching algorithm to select representative SCGraphs from the data set to build vulnerability SCGraph libraries. Second, determine whether the contract contains vulnerabilities by calculating the similarity between the SCGraphs generated from the contracts to be detected and the SCGraphs in the vulnerability library. Experiments show that our approach achieves an inspiring high detection rate and is the fastest among existing vulnerability detection tools, which indicates that it can provide good vulnerability detection for smart contracts.