Research on Online Log Anomaly Detection Model Based on Informer

ABSTRACT To address the limitations of conventional reactive log anomaly detection in high‐availability systems, this paper presents OADS—an online anomaly detection system that synergizes time‐series prediction with real‐time detection. The system features LSP‐Informer, a multivariate log sequence predictor built upon Informer architecture and enhanced by a novel weighted combination loss (WCL) that simultaneously optimizes both prediction accuracy and semantic consistency. Furthermore, OADS implements a unique prediction‐detection cascade by integrating LSP‐Informer with a Temporal Convolutional Network + Attention (TCNA)‐based Log Anomaly Detection Model (LADM), enabling proactive anomaly forecasting 5–10 steps ahead. Experimental results on HDFS logs demonstrate exceptional performance: The TCNA‐based LADM achieves an F1‐score of 0.9860, while LSP‐Informer maintains a 0.9801 F1‐score for 5‐step‐ahead prediction. The complete OADS system successfully predicts potential anomalies in advance, maintaining a robust 0.73+ Jaccard index under heavy masking conditions while preserving interpretability in real‐world deployments.