Correlation And Visualization Between Energy, Attacks, and Risks (CAVBEAR)
SPAWAR SSC PACIFIC
Project Overview: Microgrids present a number of challenges - notably in availability, energy efficiency, and interoperability - and the addition of cyber security concerns for Internet-connected microgrid components increases both the scope and complexity of these issues. Introduction of controls to limit cyber exposure of microgrid systems can help mitigate security issues, but introduce new concerns as now the system as a whole potentially has different energy requirements and efficiencies. With integration of energy data and cyber events, a much more detailed picture of microgrid energy and security performance emerges.
Correlation And Visualization Between Energy, Attacks, and Risks (CAVBEAR) seeks to explore
correlations between a variety of cyber and energy related factors through a variety of both proven and novel visualizations. This will be achieved through research in several areas: 1) experimentation in a lab setting on the effects of different types of cyber-attacks on a simulated microgrid, with particular attention paid to the change in power consumption of the microgrid as the attacks progress, 2) the effects of tightened cyber security controls on energy efficiency of microgrids, 3) development of an engine to explore the correlation between energy, cyber-attack, and cyber-defense events as they occur, and 4) an intuitive visualization system that allows a microgrid manager to quickly assess a microgrid for energy efficiency in the context of various cyber events, identify specific energy events that have a cyber impact (or vice-versa), and develop plans for future incidents. This ultimately will provide the microgrid manager both a tool for managing operations, but also a decision aid through deeper understanding of the relationship between energy and cyber security.
Intern Responsibilities: Conduct cyber-focused side-channel lab experiments on a variety of devices, including documentation of configuration, process, and results. Continue development on machine learning algorithms, using experimental data for training and testing. Continue development on 3D user interface, allowing clear visualization of data features and findings.
Considered Major(s): Computer Science
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