面向信息物理系统的诊断驱动的异常检测
Diagnosis driven Anomaly Detection for CPS
AI总结：
本文提出诊断驱动的异常检测方法，利用深度学习异常检测为一致性诊断生成症状输入，并在模拟与真实CPS数据集上验证其优于现有模型。
摘要：
在信息物理系统（CPS）研究中，异常检测（检测异常行为）和诊断（识别根本原因）通常被视为两个独立、隔离的任务。然而，诊断算法需要症状，即在时间和空间上被隔离的异常，作为输入。因此，异常检测和诊断必须协同开发，才能为CPS中的诊断提供整体解决方案。为此，我们提出一种利用基于深度学习的异常检测来为一致性诊断（CBD）生成输入的方法。我们在一个模拟和一个真实世界的CPS数据集上评估了该方法，我们的模型相对于其他最先进模型表现出较强的性能。
英文摘要：
In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isolated tasks. However, diagnosis algorithms require symptoms, i.e. temporally and spatially isolated anomalies, as input. Thus, anomaly detection and diagnosis must be developed together to provide a holistic solution for diagnosis in CPS. We therefore propose a method for utilizing deep learning-based anomaly detection to generate inputs for Consistency-Based Diagnosis (CBD). We evaluate our approach on a simulated and a real-world CPS dataset, where our model demonstrates strong performance relative to other state-of-the-art models.
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。
