结合预测驱动数据采集的在线保形异常检测
Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition
AI总结：
本研究提出C-PP-COAD框架，通过结合合成与真实校准数据，在保证错误发现率控制的同时，降低在线异常检测对真实校准数据的依赖，经多类数据集验证有效。
摘要：
在线异常检测在网络安全、医疗保健、工业监控和电信等领域至关重要，及时识别偏离预期行为的情况可避免关键故障或安全漏洞。尽管已提出多种基于监督或无监督学习的异常评分方法，但现有唯一能对错误发现率（FDR）提供无假设保证的方法依赖于真实校准数据的连续流。为解决这一局限，我们提出上下文感知预测驱动保形在线异常检测（C-PP-COAD），这是一种新颖的原则性框架，可战略性利用合成校准数据缓解数据稀缺问题，同时基于上下文信息自适应整合真实数据。C-PP-COAD可包装任何现有异常检测方法，利用给定的任何异常评分构建主动保形p值统计量，这些统计量支持具有形式化FDR控制的在线测试，随时间维持严格可靠的异常检测性能。在合成和真实数据集（包括甲状腺功能异常检测、O-RAN冲突检测、5G网络入侵检测及O-RAN用户设备（UE）吞吐量下降检测）上开展的实验表明，C-PP-COAD在不损害已保证的FDR控制的前提下，显著降低了对真实校准数据的依赖。
英文摘要：
Online anomaly detection is essential in fields such as cybersecurity, healthcare, industrial monitoring, and telecommunications, 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, the only existing approach capable of providing assumption-free guarantees on the false discovery rate (FDR) rely on a continuous stream of real-world calibration data. To address this limitation, we introduce context-aware prediction-powered conformal online anomaly detection (C-PP-COAD), a novel principled framework that strategically leverages synthetic calibration data to mitigate data scarcity, while adaptively integrating real data based on contextual information. C-PP-COAD wraps around any existing anomaly detection method, leveraging any given anomaly score to construct active conformal p-value statistics. These statistics support online testing with formal FDR control, maintaining rigorous and reliable anomaly detection performance over time. Experiments conducted on both synthetic and real-world datasets, including thyroid dysfunction detection, O-RAN conflict detection, 5G network intrusion detection, and O-RAN UE throughput degradation detection, demonstrate that C-PP-COAD significantly reduces dependency on real calibration data without compromising guaranteed FDR control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
