CRII: CIF: Dynamic Network Event Detection with Time-Series Data

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Network data is ubiquitous in real-world applications, e.g., communication networks, social networks, computer networks, sensor networks, power grids, World Wide Web, and Internet of Things. In these applications, the collected data are usually associated with a network structure, and the network structure may correspond to communication links in wireless networks, information flows in social networks, and causal relationships between the network nodes. Compared to individual entities, network data, by their interconnected nature, contain rich causal and correlation information, and present both complication and opportunity for statistical inference. Timely detection of dynamic events as soon as they occur is a problem of great interest, especially as false alarms are possible in networks of really large sizes, and measurements taken from one part of the network may not accurately capture events in another part of the network. This project addresses this challenge and develops a comprehensive framework for dynamic event detection in networks with time-series data. The developed methodology in this project can benefit a wide range of applications, e.g., intrusion detection in computer networks, epidemic detection, seismic event detection, and fake news detection in social networks. This project will substantially advance the understanding of how to accurately model and sequentially detect an event with a dynamic nature, and how to exploit network topology for reliable and computationally efficient detection in networks. In the project, the following two thrusts will be explored: (i) detection of dynamics at a single node; and (ii) detection of dynamics in the network. Practical applications, e.g., fault detection in electric motor, dynamic community detection in social networks and seismic event detection, will be studied to validate algorithms developed in this project. Tools from probability theory, information theory and stochastic optimization will be used to develop novel methodologies that address the underlying challenges in this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.