Leveraging machine learning for the detection of structured interference in Global Navigation Satellite Systems.

ABSTRACT: Radio frequency interference disrupts services offered by Global Navigation Satellite Systems (GNSS). Spoofing is the transmission of structured interference signals intended to deceive GNSS location and timing services. The identification of spoofing is vital, especially for safety-of-life aviation services, since the receiver is unaware of counterfeit signals. Although numerous spoofing detection and mitigation techniques have been developed, spoofing attacks are becoming more sophisticated, limiting most of these methods. This study explores the application of machine learning techniques for discerning authentic signals from counterfeit ones. The investigation particularly focuses on the secure code estimation and replay (SCER) spoofing attack, one of the most challenging type of spoofi

SUBMITTER: Nabi I 

PROVIDER: S-EPMC11623110 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature