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GPS Spoofing Detection Based on Variance and Singular Values Analysis of Cross Ambiguity Function and Machine Learning Algorithms
M. J. Jahantab , S. Tohidi , Mohammad Reza Mosavi , Diego Martín De Andrés
Abstract:   (61 Views)
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.
Keywords: GPS, Spoofing Detection, CAF, SVD, MLP, kNN, Dimension Reduction Algorithm.
Full-Text [PDF 1380 kb]   (43 Downloads)    
Type of Study: Research Paper | Subject: Image Processing
Received: 2025/09/26 | Revised: 2026/05/29 | Accepted: 2026/02/19
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.