Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems

TL;DR

This paper introduces ICODE, an explainable neural ODE-based framework that detects, classifies, and localizes anomalies and their root causes in dynamical systems, enhancing interpretability and accuracy.

Contribution

The paper presents ICODE, a novel integrated model that combines anomaly detection, root cause analysis, and classification within an interpretable neural ODE framework for dynamical systems.

Findings

ICODE accurately detects anomalies in various systems.

It effectively classifies anomaly types as cyber or measurement.

The method successfully localizes root causes in time series data.

Abstract

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.