Large-Scale Network Inference: Detecting the Unknown and the Intermittent
Abstract
Accurate and timely detection of network anomalies or unusual activities, be it endogenous (caused by internal malfunctioning components) or exogenous (caused by hostile attacks and intrusions), is crucial to the functionality and survivability of tactical military networks. The objective of this research is to develop general design methodologies for large-scale network inference for anomaly detection. We aim to establish fundamental limits on sample complexityÑin particular, the scaling behavior of sample complexity with respect to the problem size and the detection accuracyÑand develop efficient algorithms that achieve or approach the fundamental limits with scalable low-complexity implementations. Our emphasis is on low-complexity deterministic strategies with implementations scalable to large networks.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 06, 2018
- Source ID
- W911NF1710464
Entities
People
- Qing Zhao
Organizations
- Army Contracting Command
- Cornell University
- United States Army