A Conditional Diffusion-based Data Augmentation for Anomaly Detection in AIOps

Data augmentation plays a crucial role in AIOps for enhancing the performance of classification models in scenarios with limited supervision. However, current methods used for generating pseudo-anomaly samples may fail in AIOps: existing data augmentation methods suffer from poor sample quality due to class imbalance, high dimensionality, and high diversity. Inspired by the conditional DDPM, we address the problem by generating realistic anomaly samples between normal and abnormal ones. Unfortunately, due to the lack of pre-trained encoders and the difficulty of determining conditional information, it is hard to directly use conditional DDPM. In this work, we present C-Aug which combines sample mixing and conditional diffusion to overcome the above issues. C-Aug respectively achieves F1-Scores of 0.76, 0.98, and 0.90 on three public datasets, which significantly outperforms the other five baselines.