R.L. Breiger -- Current funded research projects

New Analytic Methods for the Exploitation of Open-Source Structured Databases on the Pursuit of WMD Terrorism. Basic Research Grant awarded by the Defense Threat Reduction Agency (DTRA), 2010-2016. Click here for the project’s website.

Ronald L. Breiger, Principal Investigator. Co-Principal Investigators: Gary Ackerman, Victor Asal, H. Brinton Milward, R. Karl Rethemeyer. This project is being conducted by the University of Arizona in partnership with the University at Albany-SUNY and the National Consortium for the Study of Terrorism and Responses to Terrorism (START) at the University of Maryland. The research seeks to enhance and leverage existing open-source datasets on violent non-state actors in order to develop new analytical tools to model human networks engaged in the pursuit of chemical, biological, radiological, and nuclear (CBRN) weapons.

Securing Cyber Space: Understanding the Cyber Attackers and Attacks via Social Media Analytics. Research grant awarded  by the National Science Foundation (NSF), Program on Secure and Trustworthy Cyberspace (SaTC), 2013-2016. Click here for grant website.

Hsinchun Chen, Principal Investigator. Co-Principal Investigators: Ronald L. Breiger, Salim Hariri, Thomas Holt. Web mining and machine learning technologies are used in tandem with social science methodologies to study hacker community structure, content, and behaviors; markets; artifacts; and cultural differences.

Human-Centric Predictive Analytics of Cyber-Threats: A Temporal Dynamics Approach.

Early-concept Grant for Exploratory Research (EAGER) awarded by the National Science Foundation (NSF), Program on Secure and Trustworthy Cyberspace (SaTC), 2013-2015. Click here for grant website.

H. Brinton Milward, Principal Investigator.  Co-Principal Investigators: Ronald L. Breiger, Loukas Lazos, Jerzy W. Rozenblit. The three major activities of this project are as follows: (a) comprehensive models of cyber-attack characteristics are developed using feature extraction techniques on diverse data sources, (b) adversarial groups are classified according to their feature similarities, and (c) social network models and tools, as well as case studies, are applied to infer adversarial group typology.

Inferring Structure and Forecasting Dynamics on Evolving Networks. Multidisciplinary University Research Initiative (MURI) Grant awarded by the Air Force Office of Scientific Research (AFOSR), 2010-2015. Click here for the project’s website.

Participating institutions are the University of California, Los Angeles; the University of Arizona; the University of Southern California; the University of California, Santa Barbara; the University of California, Irvine; and Claremont Graduate University.

Principal Investigator: P. Jeffrey Brantingham, UCLA. Co-Principal Investigators at the University of Arizona are Paul Cohen, Ronald Breiger, and H. Brinton Milward. The principal tasks of this project are development of stable metrics for inferring latent properties of social networks; forecasting of dynamical processes operating on evolving networks; and planning and predicting outcomes of interventions on network structure and function.