统一动态系统中的可解释异常检测与根因分析
Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems
AI总结：
本文提出可解释因果常微分方程网络ICODE，在单一框架内同时实现动态系统的异常检测、根因分析与异常类型分类，并通过因果推断揭示异常来源。
摘要：
动态系统广泛存在于各种科学和工程领域，容易受到异常的影响，这些异常会显著影响其性能和可靠性。本文针对由常微分方程（ODEs）控制的动态系统中的异常检测、根因定位和异常类型分类等关键挑战展开研究。我们定义了两类异常：网络异常，通过相互关联的变量传播；测量异常，仅局限于单个变量。为应对这些挑战，我们提出了可解释因果常微分方程（ICODE）网络，这是一种模型内在可解释的学习框架。ICODE利用神经常微分方程进行异常检测，同时通过解释通道进行因果推断以执行根因分析（RCA），阐明为什么特定时间段被标记为异常。ICODE旨在单一可解释框架内同时执行异常检测、根因分析和异常类型分类。我们的方法基于以下假设：异常会改变系统的底层常微分方程，表现为变量之间因果关系的变化。我们提供了理论分析，说明如何利用学习模型参数中的扰动来识别时间序列数据中的异常及其根因。全面的实验评估证明了ICODE在各种动态系统中的有效性，展示了其准确检测异常、分类异常类型并定位异常来源的能力。
英文摘要：
Dynamical systems, prevalent in various scientific and engineering domains, are susceptible to anomalies that can significantly impact their performance and reliability. This paper addresses the critical challenges of anomaly detection, root cause localization, and anomaly type classification in dynamical systems governed by ordinary differential equations (ODEs). We define two categories of anomalies: cyber anomalies, which propagate through interconnected variables, and measurement anomalies, which remain localized to individual variables. To address these challenges, we propose the Interpretable Causality Ordinary Differential Equation (ICODE) Networks, a model-intrinsic explainable learning framework. ICODE leverages Neural ODEs for anomaly detection while employing causality inference through an explanation channel to perform root cause analysis (RCA), elucidating why specific time periods are flagged as anomalous. ICODE is designed to simultaneously perform anomaly detection, RCA, and anomaly type classification within a single, interpretable framework. Our approach is grounded in the hypothesis that anomalies alter the underlying ODEs of the system, manifesting as changes in causal relationships between variables. We provide a theoretical analysis of how perturbations in learned model parameters can be utilized to identify anomalies and their root causes in time series data. Comprehensive experimental evaluations demonstrate the efficacy of ICODE across various dynamical systems, showcasing its ability to accurately detect anomalies, classify their types, and pinpoint their origins.
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。
