Cyber-threats analytics for detection of GNSS spoofing

Spoofing of the Global Navigation Satellite System (GNSS) open service (unencrypted) signal is of continuous interest to professionals and non-professional users. The main reason for this is the risk of unaware use of manipulated GNSS data, which becomes extremely relevant in all Safety-of-Life (SOL) Position-Navigation-Timing (PNT) applications, such as aircraft navigation or high precision time synchronization of traffic control systems. In this paper, we aim to develop an approach to detect spoofing of the GNSS signal based on the machine learning technique. The developed approach shows high potential in detecting the spoofed signal in the sequence of the non-spoofed GNSS signals by achieving the success rate of 96%