Air Force surveys industry for the latest machine learning techniques able to resist cyber attack

ROME, N.Y. – U.S. Air Force researchers are surveying industry for the state of the art in robust and secure military machine learning technology that is impervious to cyber attack that could reduce confidence in the machine's decisions.

Jun 5th, 2018
Air Force surveys industry for the latest machine learning techniques able to resist cyber attack
Air Force surveys industry for the latest machine learning techniques able to resist cyber attack
ROME, N.Y. – U.S. Air Force researchers are surveying industry for the state of the art in robust and secure military machine learning technology that is impervious to cyber attack that could reduce confidence in the machine's decisions.

Officials of the Air Force Research Laboratory Information Directorate in Rome, N.Y., have released a request for information (RFI-AFRL-RIK-18-04) for the Robust and Secure Machine Learning project.

Machine learning experts looking for information to better understand existing vendor offerings and the landscape of research and development towards robust and secure machine learning techniques.

Researchers want to learn how determine what algorithms, methods, and techniques will be necessary to ensure efficient and effective performance of current and future machine learning systems.

Today's advanced machine learning algorithms can be vulnerable to adversarial attacks that can machine learning systems to make wrong determinations, or reduce operator confidence in the machine's classifications. Even though computer scientists have come up ways to resist these attacks, enemies usually find ways defeat them, researchers say.

Related: Air Force to develop tool for military intelligence analysis and decision-making

Among the goals of this project is gain deeper knowledge of machine learning model architectures to understand the root cause of identified vulnerabilities and identify intelligent design tradeoffs.

Researchers would like to evaluate the performance of proposed solutions on Air Force datasets to include video and image classification, communications, and cyber operations.

Companies interested should email 8-page responses no later than 30 June 2018 to the Air Force's Ryan Luley at ryan.luley@us.af.mil.

For questions or concerns contact Ryan Luley by email at ryan.luley@us.af.mil, or by phone at 315-330-3848. Also contact Gail Marsh by email at gail.marsh@us.af.mil, or by phone at 315-330-7518.

More information is online at https://www.fbo.gov/spg/USAF/AFMC/AFRLRRS/RFI-AFRL-RIK-18-04/listing.html.

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