Context-Aware Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition
This addresses data scarcity issues for applications like cybersecurity and healthcare, though it is incremental as it builds on existing conformal methods.
The paper tackles the problem of limited real calibration data in online anomaly detection by introducing a framework that uses synthetic data and contextual cues to maintain false discovery rate (FDR) control, achieving significant reduction in dependency on real data without compromising guarantees.
Online anomaly detection is essential in fields such as cybersecurity, healthcare, and industrial monitoring, where promptly identifying deviations from expected behavior can avert critical failures or security breaches. While numerous anomaly scoring methods based on supervised or unsupervised learning have been proposed, current approaches typically rely on a continuous stream of real-world calibration data to provide assumption-free guarantees on the false discovery rate (FDR). To address the inherent challenges posed by limited real calibration data, we introduce context-aware prediction-powered conformal online anomaly detection (C-PP-COAD). Our framework strategically leverages synthetic calibration data to mitigate data scarcity, while adaptively integrating real data based on contextual cues. C-PP-COAD utilizes conformal p-values, active p-value statistics, and online FDR control mechanisms to maintain rigorous and reliable anomaly detection performance over time. Experiments conducted on both synthetic and real-world datasets demonstrate that C-PP-COAD significantly reduces dependency on real calibration data without compromising guaranteed FDR control.