Context-Aware Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition

TL;DR

This paper introduces C-PP-COAD, a novel online anomaly detection framework that combines synthetic and real data, leveraging conformal p-values and adaptive strategies to ensure reliable detection with minimal real calibration data.

Contribution

It proposes a context-aware, prediction-powered conformal online anomaly detection method that reduces reliance on real calibration data while maintaining FDR guarantees.

Findings

Significantly reduces dependence on real calibration data.

Maintains rigorous FDR control in online anomaly detection.

Effective on both synthetic and real-world datasets.

Abstract

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.