Improving Lithium-Ion Battery Supply Chain Information Security by User Behavior Monitoring Algorithm Incorporated in Cloud Enterprise Resource Planning

Abstract

Cloud enterprise resource planning (Cloud ERP) provides an efficient big data management solution for lithium-ion battery (LiB) enterprises. However, in the open ecological environment, Cloud ERP makes the LiB supply chain face multi-user and multi-subject interactions, which can generate sensitive data and privacy data security issues (such as user override access behavior). In this study, we take the value and information interaction into account to examine the user behaviors of the diverse stakeholders in the LiB supply chain. Therefore, a user behavior monitoring algorithm (UBMA), different from the mainstream supervised algorithms and unsupervised learning algorithms, is proposed to monitor the unsafe behaviors that may threaten data privacy in Cloud ERP. The results show that the UBMA can accurately search out the user behavior sequence where the unsafe behavior is located from a large amount of user behavior information, which reduces the complexity of directly identifying the unsafe behavior. In addition, compared with the recursive unsupervised binary classification method, the UBMA model has a lower resource consumption and higher efficiency. In addition, the UBMA has great flexibility. The UBMA can be further updated and extended by re-establishing the statistical characteristics of the standard user behavior fields to quickly adapt to user changes and function upgrades in the LiB supply chain.