Discuss the development and application of a mathematical and computational framework for detecting and classifying weak, distributed patterns in sensor networks. The work being done at Numerica demonstrates the effectiveness of space-time inference on graphs, robust matrix completion and second order analysis in the detection and classification of distributed patterns that are not discernible at the level of individual nodes. Our focus is on cyber security scenarios where computer nodes (such as terminals, routers and servers) are sensors that provide measurements of packet rates, user activity, central processing unit usage, etc. When viewed independently, they cannot provide a definitive determination of the underlying pattern, but when fused with data from across the network – both spatially and temporally – the relevant patterns emerge. The clear underlying suggestion is that only detectors and classifiers that use a rigorous mathematical analysis of temporal measurements at many spatially distributed points in the network can identify network attacks. This research builds upon work in compressed sensing and robust matrix completion and is an excellent example of industry-academic collaboration.
Thursday, February 03, 2011
Free and open to the public