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

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.

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

The paper "Context-Aware Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition" focuses on enhancing the efficacy of anomaly detection systems by addressing the challenges posed by limited real-world calibration data. The proposed framework, termed C-PP-COAD, introduces a novel approach to mitigate data scarcity by strategically leveraging synthetic calibration data while maintaining rigorous statistical guarantees on the false discovery rate (FDR).

Overview and Methodology

Anomaly detection is critical in various domains including cybersecurity, healthcare, and telecommunications, where early identification of deviations from expected behavior can prevent failures or security breaches. Traditionally, anomaly detection techniques rely on continuous streams of real-world calibration data to recalibrate scoring functions, which is challenging due to data limitations. This paper seeks to alleviate these issues with the introduction of C-PP-COAD.

C-PP-COAD extends existing conformal online anomaly detection (COAD) methods by integrating synthetic data along with contextual information. This integration aims to reduce dependency on real data without compromising statistical guarantees. C-PP-COAD computes prediction-based conformal p-values using synthetic data and adaptively decides whether real calibration data is necessary based on contextual cues, supporting the control of sFDR.

Key Contributions

1. Integration of Synthetic Data: Unlike previous methods that use synthetic data primarily for augmentation, C-PP-COAD employs synthetic data for real-time calibration. It utilizes active p-values, which enable the adaptive query of true statistics, thus reducing reliance on real-world data.
2. Context-Based Data Acquisition: C-PP-COAD employs contextual information to determine the necessity of acquiring real calibration data, optimizing the balance between detection accuracy and operational cost.
3. Statistical Guarantees: It offers robust sFDR control utilizing decaying-memory metrics, ensuring statistical reliability across time.
4. Handling Missing Data: C-PP-COAD incorporates mechanisms to operate effectively on incomplete datasets, preserving its statistical validity.

Implications and Future Directions

The implications of this research are both practical and theoretical. Practically, C-PP-COAD offers a cost-effective solution to anomaly detection challenges where data scarcity is a concern, thereby enhancing performance in real-world monitoring systems across various industries. The framework's ability to provide statistical guarantees while improving detection power and data efficiency showcases its suitability for deployment in dynamic environments with evolving data characteristics.

Theoretically, the paper's contribution to the field of anomaly detection lies in the innovative use of synthetic data and contextual information combined with conformal prediction methodologies. This opens up possibilities for future research into more refined synthetic data generation techniques and context-specific adaptation strategies that could further improve the power and efficiency of anomaly detection systems.

Overall, the development of C-PP-COAD is timely and relevant, considering the increasing complexity and data scarcity challenges faced by modern monitoring systems. Future work may explore deeper evaluations of context-aware synthetic data quality and adaptive querying mechanisms, potentially expanding the applicability of this framework to broader use cases and more complex environments in artificial intelligence systems.