Space-Time Signal Processing for Distributed Pattern Detection in Sensor Networks

We present a theory and algorithm for detecting and classifying weak, distributed patterns in network data that provide actionable information with quantiable measures of uncertainty. Our work demonstrates the eectiveness of space-time inference on graphs, robust matrix completion, and second order analysis for the detection of distributed patterns that are not discernible at the level of individual nodes. Motivated by the importance of the problem, we are specically interested in detecting weak patterns in computer networks related to Cyber Situational Awareness. Our focus is on scenarios where the nodes (terminals, routers, servers, etc.) are sensors that provide measurements (of packet rates, user activity, central processing unit usage, etc.) that, when viewed independently, cannot provide a denitive determination of the underlying pattern, but when fused with data from across the network both spatially and temporally, the relevant patterns emerge. The approach is applicable to many types of sensor networks including computer networks, wireless networks, mobile sensor networks, and social networks, as well as in contexts such as databases and disease outbreaks.