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Intelligent Detection for Key Performance Indicators in Industrial-Based Cyber-Physical Systems

机译：基于工业网络 - 物理系统的关键性能指标智能检测

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摘要

Intelligent anomaly detection for key performance indicators (KPIs) is important for keeping services reliable in industrial-based cyber-physical systems (CPS). However, it is common in practice for various KPI sampling strategies to be utilized. We experimentally verify that anomaly detection is highly sensitive to irregular sampling, and accordingly go on to investigate low-cost anomaly detection for large-scale irregular KPIs. Irregular KPIs can be classified into four types: equal interval and unequal quantity (EIUQ) KPIs, unequal interval (UI) KPIs, unequal interval with equal duration (UIED) KPIs, and segmented irregular KPIs. In this article, we propose an anomaly detection framework based on these irregular types. Moreover, to handle the various lengths and phase shifts among EIUQ KPIs, we propose a normalized version of unequal cross-correlation, which slides the KPIs to enable finding the most similar position. To avoid high computational costs, we analyze the low-rank feature of KPIs data and propose a matrix factorization-based alignment algorithm for UIED KPIs; this algorithm treats UIED KPIs as an incomplete matrix and recovers the KPIs to align them before performing anomaly detection. Extensive simulations using three public datasets and two real-world datasets demonstrate that our algorithm can achieve a larger F1-score than Minkowski distance and less time than dynamic time warping distance.
机译：关键绩效指标（KPI）智能异常检测是保持可靠的服务基于工业网络物理系统（CPS）的重要。然而，通常在实践中被使用的各种KPI取样策略。我们通过实验验证异常检测是不规则的采样高度敏感，并据此继续进行调查的大型不规则的KPI低成本的异常检测。不规则的KPI可以分为四种类型：等间隔和不等量（EIUQ）的KPI，不等间隔（UI）的KPI，不等间隔具有相等的持续时间（UIED）KPI和分段不规则的KPI。在这篇文章中，我们提出了一种基于这些不规则类型的异常检测框架。此外，为了处理EIUQ的KPI之间的各种长度和相移，我们提出不等的互相关，其滑动的KPI来使找到最类似的位置的归一化版本。为了避免高计算成本，我们分析的KPI数据的低级别功能，并提出了UIED KPI的一个基于矩阵分解，比对算法;该算法对待UIED的KPI作为一个不完整的矩阵，并恢复执行的KPI异常检测之前，将它们对齐。使用三个公共数据集和两个真实世界的数据集大量的模拟表明，我们的算法可以实现更大的F1-得分比闵可夫斯基距离，比动态时间规整距离时间要少。

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