CCRisk: Automated Risk Detection on Heterogeneous Consortium Chains for Supply Chain Finance

The consortium blockchain aims to provide a secure and trusted digital platform for data sharing among multiple organizations, in which, participants are typically groups with common interests and are allowed to manage the network, reach consensus, and share data. However, a prominent issue is the lack of evaluation metrics and assessment methods for consortium blockchains. Accurately measuring consortium blockchains’ performance, security, and consistency is still challenging. This paper proposes an automated risk detection tool for heterogeneous consortium blockchains, namely CCRisk, which can be adapted to multiple consortium blockchain–based services such as Supply Chain Finance, enabling full-process automation of detection. It begins by constructing risk indicators for various heterogeneous consortium blockchain technologies. A chaos engineering-based risk indicator detection method and an abstract syntax tree–based smart contract risk detection method are proposed based on these indicators. We implement this tool to examine three widely adopted consortium blockchains—Hyperledger Fabric, Fisco-bcos, and Chainmaker. Experiment results demonstrate its ability to accurately detect risks in different consortium blockchain networks.