BAE Systems has been awarded a $14 million contract from the Intelligence Advanced Research Projects Activity (IARPA) to develop tools to decipher an ever-growing number of radio frequency (RF) signals in order to quickly and accurately help secure mission-critical information.
BAE Systems will advance machine learning and artificial intelligence technology and techniques to identify signals in the RF spectrum under the terms of the contract, which is part of the Securing Compartmented Information with Smart Radio Systems (SCISRS) program. The technology will provide enhanced situational awareness, help to target threats, and secure communications against malicious attacks.
“In uncontrolled environments, secure communications can be jeopardized by RF signals that are almost impossible to manually find and identify in real time,” said Scott Kuzdeba, chief scientist for BAE Systems’ FAST Labs™ research and development organization. “Our technology will identify RF signals in increasingly crowded electromagnetic spectrum environments, providing commercial or military users with greater automated situational awareness of their operating environment.”
Intelligence Community and Department of Defense missions require that information and data be securely generated, stored, used, transmitted, and received. This needs to be the case even when originating in unsecured and uncontrolled environments. The goal of the SCISRS program is to develop smart radio techniques to automatically understand these environments in order to enable securing our data, including detecting and characterizing complex RF anomalies and unexpected signals. The specific types of anomalies include hidden, altered, or mimicked signals, and abnormal unintended emissions.
The program, which includes collaboration with subcontractors PFP Cybersecurity, Intelligent Automation (a BlueHalo Company), Signal Processing Technologies, and Virginia Polytechnic Institute and State University, will leverage BAE Systems’ autonomy portfolio and build on the company’s work on DARPA’s Radio Frequency Machine Learning System (RFMLS) program.
Ref. No. 015/2022