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EAGER: Collaborative Research: Algorithmic Framework for Anomaly Detection in Interdependent Networks

Principal Investigator: 
Co-Principal Investigator: 
Lazaros Gallos
Grant Agency: 
National Science Foundation
Grant Duration: 
09/01/2016 to 08/31/2018

This EAGER project initiates the rigorous analysis of the different challenges and opportunities for anomaly detection posed by multilayer networks relative to single network structures, with a particular focus on how cross-layer information can be effectively used to improve both efficiency and detection as well as how cross-layer threats can create vulnerabilities. The framework builds on recent results of the Rutgers and U. Tennessee team on anomaly detection and the recent results of project collaborators at the U. Bar-Ilan on resilience and function of multilayer networks. Sharing of non-sensitive summary information is used to improve early detection of threats by combining accumulated information from different participants within and across single networks in the multilayer network of interacting infrastructures. In addition to addressing cyber security, this framework serves as a prototype to study anomalies, including attacks, in different physical and virtual systems, such as social or information networks.  This grant is collaborative with a grant to PI Nina Fefferman at the University of Tennessee.