Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations.
Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations.
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DOI：
10.1145/2939672.2939765
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发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Wang W
中科院分区：
文献类型：
--
作者：
Cheng W;Zhang K;Chen H;Jiang G;Chen Z;Wang W
现代世界已经见证了我们从大规模信息系统和网络物理系统中收集、传输和分发实时监控和监视数据的能力的急剧增长。因此，检测系统异常吸引了许多领域，如安全，故障管理和工业优化的显着量的兴趣。最近，不变网络已被证明是一个强大的方式来描述复杂的系统行为。在不变网络中，节点表示系统组件，边表示两个组件之间稳定、重要的交互。不变性网络的结构和演化，特别是消失的相关性，可以为定位因果异常和进行诊断提供重要的启示。然而，利用不变网络检测因果异常的现有方法通常使用消失相关性的百分比来对可能的因果分量进行排名，这具有几个限制：1）忽略了网络中的故障传播; 2）根因果异常可能不总是具有高百分比消失相关性的节点; 3）消失相关性的时间模式没有被用于鲁棒检测。为了解决这些局限性，在本文中，我们提出了一个网络扩散为基础的框架，以确定显着的因果异常和排名。我们的方法可以有效地在整个不变网络的故障传播模型，并可以进行联合推理的结构，和时间演变的破坏不变性模式。因此，它可以定位真正导致相关性消失的高置信度异常，并可以补偿系统中的非结构化测量噪声。在人工数据集、银行信息系统数据集和火电厂信息物理系统数据集上的实验证明了该方法的有效性。
Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1) fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a high-percentage of vanishing correlations; 3) temporal patterns of vanishing correlations are not exploited for robust detection. To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system. Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets demonstrate the effectiveness of our approach.
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