CAE-IF: An Anomaly Detection Approach Based on Temporal Representation of the Reconstruction Error

In the field of network traffic anomaly detection, unsupervised learning plays a critical role yet encounters significant challenges, including accurately determining anomaly thresholds and modeling the intricate temporal dynamics of network traffic. To address these challenges, we present a novel a