An Adaptive Modeling Framework for Bivariate Data Streams with Applications to Change Detection in Cyber-Physical Systems

Cyber-physical systems - systems that incorporate physical devices with cyber components - are appearing in diverse applications, and due to advances in data acquisition, are accompanied with large amounts of data. The interplay between the cyber and the physical components leaves such systems vulnerable to faults and intrusions, motivating the development of a general model that can efficiently and continuously monitor a cyber-physical system. To be of practical value, the model should be adaptive and equipped with the ability to detect changes in the system. This paper makes three contributions: (1) a new adaptive modeling framework for monitoring an arbitrary cyber-physical system in real-time using a flexible statistical distribution called the normal-gamma; (2) a novel streaming validation procedure, demonstrated on data streams from a cyber-physical system at Los Alamos National Laboratory, to justify the use of the normal-gamma and our new adaptive modeling approach; and (3) a new online change detection algorithm demonstrated on synthetic normal-gamma data streams.