Global Navigation Satellite System Spoofing Attack Detection Using Receiver Independent Exchange Format Data and Long Short-Term Memory Algorithm

Global Navigation Satellite Systems (GNSS) are widely used for positioning, navigation, and timing (PNT) applications, making them a critical infrastructure component. However, GNSS signals and receivers are vulnerable to several attacks that can expose the users to serious threats. The GNSS spoofing attack, for example, is one of the most widespread in this domain and is used to manipulate positioning and timing data by transmitting counterfeit signals. Thus, in this study, we propose a method for analyzing and detecting anomalies in RINEX observation data that is associated with spoofing attacks. The proposed method is based on Long Short-Term Memory (LSTM) networks and focuses on the observation parameters defined by the RINEX standard, which are computed in the Measurements block and subsequently used in the Navigation block of a GNSS receiver architecture. Attack detection involves processing GNSS observation codes and learning the temporal dependencies necessary to identify anomalies associated with GNSS signal spoofing. During the testing phase, the proposed method was applied to GNSS observation codes affected by spoofing, using an LSTM-based reconstruction approach. An ensemble strategy across grouped observation codes was used to identify temporal inconsistencies indicative of anomalies.