Deep Learning Approach to Structure-Temporal Collaborative Anomaly Detection in Microservice Architectures

This paper proposes a structure-temporal collaborative anomaly detection method to address the challenges of strong structural dynamics, complex service dependencies, and diverse temporal behavior patterns in microservice architectures. The proposed method includes two core modules: Structure-Aware Dependency Modeling (SADM) and Multi-Channel Temporal Representation (MCTR). The SADM module constructs a service invocation dependency graph and employs graph neural networks to jointly model node behavior and topological structure, enhancing the model’s ability to represent structural disturbances and abnormal paths. The MCTR module designs multiple heterogeneous temporal modeling paths, including local convolution, dilated convolution, and residual diffusion mechanisms, to capture dynamic behavioral changes across different time scales. These features are effectively fused through a channel attention mechanism to improve the model’s ability to distinguish complex anomaly patterns. Experiments are conducted on public microservice datasets, including comparative and ablation studies. The results show that the proposed model outperforms existing methods in key metrics such as Precision, Recall, F1-score, and AUROC. Further sensitivity analysis verifies the impact of different structural parameters and temporal modeling configurations on model performance, demonstrating the effectiveness and robustness of the proposed method in structural and temporal modeling.