Annotation. This article explores the perspective opportunities for securely broadcasting the pulse signals emitted by the ADS-B (Automatic Dependent Surveillance–Broadcast) system, which is currently used in modern aviation systems, against external cyber threats. The objective is to enhance the security level of the existing infrastructure, optimize frequency assessment, and improve traffic management through predictive modeling, thereby enabling more efficient and proactive control. The proposed integration architecture employs deep learning algorithms to analyze aircraft signals and provides functionalities such as signal congestion management, real-time risk forecasting, and proactive prediction of weather and traffic changes. Furthermore, the article presents a performance evaluation of the system operating at 1090 MHz and 978 MHz frequencies and proposes methods for frequency optimization. Research results indicate that incorporating the recognition of device identification via radiometric fingerprints into the ADS-B platform not only enhances security and operational efficiency but also significantly improves the system’s adaptability and responsiveness. This approach opens new avenues for the development of smarter and more predictable future aviation networks.

Keywords: ADS-B technology, aviation safety, frequency optimization, real-time data processing, air traffic control.