M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Diego Martín de Andrés,
Volume 22, Issue 0 (In Press 2026)
Abstract
Global Positioning System (GPS) spoofing poses serious threats to navigation systems, as it transmits false GPS signals that cause receivers to compute incorrect positions. To address this issue, our research in this study focused on leveraging the Cross-Ambiguity Function (CAF) along with advanced machine learning techniques to effectively detect spoofing attacks. A further challenge in using CAF for spoofing detection is its high dimensionality, which demands powerful hardware and considerably slows down the detection process. Detecting spoofing signals with delays of less than 0.5 chips relative to the authentic signal is particularly difficult. To overcome this, the SVD_Var dimensionality reduction algorithm, which leverages the variance of CAF data through Singular Value Decomposition (SVD), is proposed to enhance both speed and detection performance. The reduced-dimensionality data are subsequently used to train a basic Multi-Layer Perceptron (MLP) neural network and the k-Nearest Neighbors (kNN) algorithm. The effectiveness of the proposed method is validated using the widely recognized Texas Spoofing Test Battery (TEXBAT) dataset. Results indicate that the method achieves an average detection rate exceeding 80% across various TEXBAT scenarios, demonstrating enhanced sensitivity and robustness in spoofing detection compared to both traditional and state-of-the-art approaches. Also, this approach accomplishes a dimensionality reduction ranging from 99.69% to 99.99% in terms of the number of pixels which significantly accelerates the processing speed.
Maryam Moazedi, Mohammad Reza Mosavi, Diego Martín de Andrés,
Volume 22, Issue 3 (September 2026)
Abstract
The Receiver Autonomous Integrity Monitoring (RAIM) method uses additional information to detect and remove spoofing signals by analyzing pseudo-range measurements. Therefore, assuming that spoofing signals are errors for the valid signal, RAIM can be a practical method that does not impose expensive hardware to the receiver. Typically, RAIM operates under the assumption that simultaneous multi-satellite errors are highly unlikely. For example, GPS satellite errors occur no more than three times per year. Some enhanced RAIM methods have been proposed in recent years that employ additional measurements, such as Doppler shift measurements, time-differential carrier phase measurements, and so on. Since simultaneous multiple fake satellites are common in spoofing cases, basic RAIM cannot counter these types of signals, and for eliminating more than one spoofing or error signal requires additional information, such as measurements on other frequencies or satellite systems, which increases the complexity of execution. In this paper, an anti-spoofing method based on Advanced RAIM (ARAIM) has been proposed with a novel slope-based RAIM availability assessment method. Simulation results on several spoofing data sets indicate the definitive success of the proposed methods in detecting and mitigating spoofing error, with a detection success rate of over 79% using the statistical method and over 87% using the Kalman filter method.
