Detecting GPS Spoofing Attacks Using Corrected Low-Cost INS Data with an LSTM Network

With the emergence of new technologies ranging from smart cities to the Internet of Things (IoT), many objects rely on satellite-based navigation systems, such as GPS, to accomplish their tasks securely. However, GPS receivers are exposed to various unintentional and intentional attacks, threatening the availability and reliability of the delivered information. GPS spoofing is considered as one of the most dangerous attacks, where attackers transmit intense signals on the same frequency to disrupt the GPS receiver, leading to erroneous position calculations. Detection methods for GPS spoofing are crucial to ensure secure navigation. This paper proposes a method for GPS spoofing detection that utilizes artificial intelligence algorithms in combination with raw data from an inertial navigation system (INS). Since INS sensors are prone to accumulating errors over time, these inaccuracies are corrected via a Long Short-Term Memory (LSTM) algorithm. The corrected accelerations and angular rates are then compared to the accelerations and angular rates estimated from the GPS data to detect GPS spoofing signals. This comparison uses the modified M-of-N method, demonstrating its effectiveness by a detection rate reaching 80% of the spoofing zones.