Supervised Fine-Tuning for Unsupervised KPI Anomaly Detection for Mobile Web Systems

With the rapid development of cellular networks, wireless base stations (WBSes) have become crucial infrastructure for mobile web systems. To ensure service quality, operators constantly monitor the operation status of WBSes and deploy anomaly detection methods to identify anomalies promptly. After the deployment of anomaly detection methods, operators periodically collect feedback, which holds significant value in improving anomaly detection performance. In real-world industrial environments, the frequency of false negative feedback is usually very low, and the newly generated data's distribution can differ significantly from that of the original training data. Therefore, the feedback-based performance improvement of the previously proposed methods is limited. In this paper, we propose AnoTuner, which incorporates a false negative augmentation mechanism to generate similar false negative feedback cases, effectively compensating for the low feedback frequency. Additionally, we introduce a Two-Stage Active Learning (TSAL) mechanism that minimizes data contamination issues caused by the difference between the distribution of feedback data and that of the training data. Experiments conducted on the real-world data collected from a top-tier global Internet Service Provider (ISP) demonstrate that the performance improvement of AnoTuner after feedback-based fine-tuning is significantly higher than that of the best baseline method.