Abstract

A new data fusion and network analytics framework is proposed that is based on the topology of large-scale networks and the stochastic dependencies between nodes, edges, and sensor data. The framework can transform real-time sensor data collected from disparate sources in a network to detect the location of anomalies and the nodes that are impacted by the detected anomalies. By intelligently fuzing multidimensional sensor data based on the topology of a large-scale network, this article also contributes to big data analytics for network systems. We will show that the proposed framework not only brings computational benefits, but also results in better anomaly estimates leading to lower false alarm rates and higher detection rates.

摘要

提出了一种新的数据融合和网络分析框架，该框架基于大规模网络的拓扑结构以及节点、边和传感器数据之间的随机依赖关系。该框架可以转换从网络中不同来源收集的实时传感器数据，以检测异常的位置以及受检测到的异常影响的节点。通过基于大规模网络拓扑智能融合多维传感器数据，本文也为网络系统的大数据分析做出了贡献。我们将展示所提出的框架不仅带来了计算优势，而且还导致更好的异常估计，从而导致更低的误报率和更高的检测率。