Analysis of Cyber Infrastructure Authentication Failure Vulnerabilities to Inform Security Decisions

Principal Investigator: Roman Yampolskiy

Other Researchers: Anthony M. Barrett, Seth Baum


Many kinds of cyber security failure modes have been found and exploited by intelligent, adaptive adversaries.  Efforts to develop more secure systems often aim to balance security benefits against costs imposed on users.  Risk analysis and decision analysis methods hold potential for informing decisions to prioritize cyber security development efforts.    In this project, we develop and apply a model for estimating probabilities for cyber infrastructure user authentication failure modes.  Failure probability estimation provides a decision-analytic theoretical basis for assessing the benefits of development of additional authentication modalities and overall configuration of an optimal multimodal system.  We identify cyber security system failure modes, and represent those within our model.  To estimate the probabilities of each specific type of failure, we employ two main methods: first, we find and incorporate available empirical data, and second, we use expert judgment to create some approximate estimates.  For our main candidate application case, we propose to focus on systems that provide continuous authentication to cyber infrastructure by profiling multiple physical and/or behavioral biometrics of users.  We will select a specific application case in partnership with an end customer at the US Department of Homeland Security (DHS), which has significant responsibility for helping to secure U.S. critical infrastructure that have some kind of cyber vulnerabilities in their control and communication systems (including the electric power grid, telecommunications systems, chemical industry plants, and many other Critical Infrastructure/Key Resources areas).