UAC-AD: Unsupervised Adversarial Contrastive Learning for Anomaly Detection on Multi-Modal Data in Microservice Systems

Abstract

To ensure the stability and reliability of microservice systems, timely and accurate anomaly detection is of utmost importance. Recently, considering the lack of labels in real-world scenarios and the collaborative and complementary relationships of multi-modal data in reflecting system anomalies, unsupervised multi-modal anomaly methods have been proposed. However, existing methods face challenges in effectively distinguishing normal hard samples (they are normal but hard to classify correctly) from anomalies. This is mainly caused by two aspects. First, the hard sample patterns are complex. Second, the convergence speed is inconsistent between hard and simple samples. To overcome these issues, we propose an unsupervised adversarial contrastive multi-modal anomaly detection method (UAC-AD). We utilize contrastive learning to help learn the complex patterns of hard samples and enlarge the distance between hard and anomaly samples. Meanwhile, the adversarial framework automatically identifies hard samples and fine-grained adjusts the training weights to each modality part of these hard samples. In this case, The hard sample problems of two aspects can be alleviated. We extensively evaluate UAC-AD on two open-source simulated datasets and a real industrial dataset from a large communication company. Extensive experimental results demonstrate the effectiveness of our approach in anomaly detection. We also release the code and dataset for replication and future research.