EAGER

Smart electric meters comprise one key technology element in an overall strategy to modernize the nation's energy infrastructure. Smart meters capture data on household energy usage at frequent intervals and transmit those data to utility companies, who use the data to automate meter reading and billing, detect and respond to outages, and manage grid operations. Data collected over time can be used to forecast demand, understand customer behavior and develop new service and pricing plans.

Moraine Valley Community College (MVCC) seeks to perform a feasibility study of replicating and refining the Brookdale Community College (BCC) CyberCenter Program (NSF funded as DUE-1331170). Both projects design, implement and evaluate an innovative pathway from cyber talent to cyber workforce. It includes a scalable, competition-driven, hands-on education model that has a potential to help with the nation's critical shortage of cybersecurity workforce. It will incorporate on-line courses, virtual labs and access tools to decrease overall per student cost in the CyberCenter program.

This project examines the trade-offs governments face between engaging in cyber offense and defense. Governments may exploit a somewhat less secure Internet to gain intelligence and strategic advantage. But a more secure Internet encourages more participation online and reduces citizens' exposure to cybercrime. Thus governments face a difficult dilemma: Should they forego potentially useful cyber operations and work on strengthening the Internet in order to provide a global good?

Software vulnerabilities are a substantial problem that exists in part due to programmer errors. A common cause is that programmers have a cognitive gap between their understanding of what actions code will perform and the actual actions the code performs. This work takes a first step toward rigorously understanding how humans think about code to (eventually) help to dramatically reduce bugs by building more human understandable programming languages and programs.

The goal of this project is to investigate the reliability, robustness, and computationally efficiency of digital audio forensic methods under various adversarial conditions, e.g., lossy compression attack. We aim to identify and develop mathematical tools for modeling and characterizing of microphone nonlinearities (fingerprints), statistical methods for acoustic environment estimation, and system identification based framework for linking an acquisition device to the audio recording.