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NSF Invests in Cybersecurity Research

The National Science Foundation’s Secure and Trustworthy Cyberspace program is getting more support from the federal agency in the form of a $78.2 million portfolio of technology projects.

Cybersecurity concept, lock and lines

The National Science Foundation is investing heavily in its Secure and Trustworthy Cyberspace (SaTC) program with a new $78.2 million portfolio of 225 new projects in 32 states, spanning research and education on artificial intelligence, cryptography, network security, privacy and usability.

The largest of the projects is $9.98 million grant to establish the Center for Trustworthy Machine Learning (CTML) at Penn State College of Engineering. The project is a multi-institution, multi-disciplinary center that will focus on developing an understanding of the security risks of machine learning and devise the tools, metrics and methods to manage and mitigate security vulnerabilities. The project is an NSF Frontier program, which means that it is a large-scale multi-institution effort with work that crosses disciplines.

"NSF's investments in SaTC are advancing knowledge to protect cyber systems from malicious behavior, while preserving privacy and promoting usability," said Jim Kurose, NSF assistant director for Computer and Information Science and Engineering. "Our goal is to identify fundamentally new ways to design, build and operate secure cyber systems at both the systems and application levels, protect critical infrastructure, and motivate and educate individuals about security and privacy."

The new CTML will work to develop an arsenal of defensive techniques to build future systems in a safer, more secure manner. 

"Machine learning is fundamentally changing the way we live and work — from autonomous vehicles, digital assistants, to robotic manufacturing — we see computers doing complex reasoning in ways that would be considered science fiction just a decade ago," said Patrick McDaniel, lead principal investigator of the CTML project. "We have a unique opportunity at this time, before machine learning is widely deployed in critical systems, to develop the theory and practice needed for robust learning algorithms that provide rigorous and meaningful guarantees."

CTML is a collaboration with Stanford University, University of California-Berkeley, University of California-San Diego and the University of Wisconsin-Madison.

More information about CTML can be found here.

About the Author

Sara Friedman is a reporter/producer for Campus Technology, THE Journal and STEAM Universe covering education policy and a wide range of other public-sector IT topics.

Friedman is a graduate of Ithaca College, where she studied journalism, politics and international communications.

Friedman can be contacted at [email protected] or follow her on Twitter @SaraEFriedman.

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