Research on Network Data Anomaly Detection Method Under Zero Trust Model
Libin Li, Kai Ma, Jia Wang, Bin Ren, Yongjiao Cao · 2024
The network data anomaly detection technology under the Zero Trust model can predict whether the network traffic is normal and provide services for monitoring new network attacks and anomalies. This article proposes a machine learning method based on multi-feature combination under the Zero Trust model, by analyzing traffic characteristics and removing redundant traffic features. The experimental results show that the accuracy of CNN algorithm in detecting traffic anomalies based on multiple features can reach over 97%, demonstrating the feasibility of the proposed method in this article.