Microservices Anomaly Detection Based on Adaptive Wavelet Convolutional Networks and Transformer

Abstract

Microservice architecture, characterized by loose coupling, elastic scalability, and independent deployment, has become the de facto standard for cloud-native services. However, in production systems, the complex interaction topologies among microservices can easily trigger cascading failures, which severely compromise system reliability and operational stability. This paper proposes an anomaly detection method for microservices that integrates adaptive wavelet convolutional networks and Transformer architecture. Specifically, the proposed method leverages a wavelet convolutional network to preprocess time-series data, while a weighted inverse transformer combined with a frequency-band attention mechanism is utilized to capture multi-scale time–frequency features of different microservices. Moreover, a dual-branch GRU-Transformer structure is then used to model both long- and short-term dependencies as well as cross-metric correlations among services. Therefore, anomaly scores can be derived through the joint optimization of prediction and reconstruction models. To overcome the challenge of threshold determination, this paper also proposes a two-stage gradient-based optimization algorithm that adaptively converges the threshold range using finite differential methods. Experimental results show that the proposed method is effective on datasets such as SMD (Server Machine Dataset), ASD (Application Server Dataset) and the NASA Anomaly Detection Dataset SMAP, where its average F1 score on SMD exceeds that of existing approaches by 11.0%, while on the SMAP dataset, an accuracy of 96.40% is achieved. These results confirm that the proposed approach can effectively detect anomalies in industrial level microservices, thereby enhancing the stability and reliability of industrial service systems.