Uncertainty-Aware Multimodal Anomaly Detection for Microservice Systems With Active Learning

Accurate and robust anomaly detection is critical for microservice system reliability. Recent multimodal approaches have improved detection comprehensiveness by integrating metrics, logs, and traces. However, they often overlook intra-modal uncertainty from noise, ambiguity, or missing data, and inter-modal uncertainty arising from varying predictive capabilities across modalities. Additionally, extensive labeling of multimodal data remains costly. To address these limitations, we propose MUAD, an uncertainty-aware multimodal anomaly detection framework with active learning. MUAD employs a Graph-based Probabilistic Encoder (GPE) to model intra-modal uncertainty through probabilistic representations, and a Confidence-aware Fusion Mechanism (CFM) to dynamically weight modalities based on their prediction confidence. Furthermore, an active learning paradigm iteratively refines the model using high-confidence pseudo-labels and informative samples, maintaining performance under label-deficient conditions. Experiments on three benchmark datasets demonstrate MUAD achieves 98.10% average F1-score, outperforming state-of-the-art methods by up to 7.87%. Results also confirm its robustness under low-quality data and limited labels.