Log-Based Anomaly Detection with Multi-level Progressive Temporal-Semantic Fusion

Log anomaly detection plays a pivotal role in ensuring system stability and security, particularly in largescale environments characterized by the generation of log data at exceptionally high volumes and velocities. Conventional approaches often struggle to effectively filter log information and fully leverage temporal dynamics, resulting in challenges such as information loss, semantic drift, and heightened computational overhead. To overcome these limitations, we present QYXLAD, an innovative log anomaly detection framework. QYXLAD enhances log representation accuracy by seamlessly integrating temporal and semantic information. It introduces a MPMM(Multi-level Progressive Masking Mechanism)-based Feature Fusion designed to capture temporal dependencies and semantic features across diverse pattern combinations, thereby significantly improving the sensitivity and precision of anomaly detection. Furthermore, QYXLAD utilizes a Mamba-based classifier for anomaly identification. Comprehensive theoretical analysis and empirical evaluations demonstrate that QYXLAD achieves state-of-the-art performance on multiple public log datasets, surpassing existing methods in key metrics such as precision, recall, and F1-score. These results underscore the framework’s efficacy and superiority in addressing log anomaly detection challenges.