Research at CyLab

CyLab's research strategy is holistic. Seven areas of research and development have been designated, spanning a wide range of technologies, systems and users. Each project meets the criteria of one or more research areas, with an aim towards building cross-functional and multi-disciplinary solutions and leveraging cross-cutting skills from faculty across the university, such as policy development, risk management or modeling. The objective is to build a new generation of technologies that will lead to measurable, available, secure, trustworthy, and sustainable computing and communications systems, as well as associated management and policy tools that enable successful exploitation of the new technologies. chip

Research Areas

 

Cross-Cutting Thrusts

The following areas of expertise are applied to research projects by leveraging the multi-disciplinary skills of faculty and graduate students at CyLab and across Carnegie Mellon. screen

CyLab in the headlines

Limiting Risks Found in the Cloud - June 10, 2013
"We're hoping that the cloud service providers understand insider threat," Carnegie Mellon CyLab researcher Dawn Cappelli says. "We have recommendations that we provide for organizations for what they should do to protect themselves against rogue administrators and to protect themselves against theft of intellectual property. Our hope is that cloud service providers understand that as well."

Those meters that rate password strength work, until they don't - June 11, 2013
"Passwords are not going to disappear overnight, or in the next 10 years or 20 years," said Lujo Bauer, researcher at Carnegie Mellon CyLab. Bauer and colleagues at Carnegie Mellon conducted the study with 2,931 subjects who created passwords on sites using one of 14 types of meters with different displays and criteria for determining strength.

“Hallucinating” a face, new software could have ID’d Boston bomber - May 29, 2013
Dr. Marios Savvides, the director of the CyLab Biometrics Center, said that the new technology could generate results much more detailed than those made by traditional image enhancement approaches. "The traditional methods yield about a 2 times to 4 times improvement" in the resolution of a facial image, he said. "This method gets us 16 times the resolution."

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