en 5-Image Processing Image Processing Research Paper Research Paper <span style="font-size:10.0pt"><span style="font-family:&quot;Times New Roman&quot;,serif">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.</span></span> GPS, Spoofing Detection, CAF, SVD, MLP, kNN, Dimension Reduction Algorithm. 4147 4147 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-78-39&slc_lang=en&sid=1 M. J. Jahantab jahantab_m77@elec.iust.ac.ir 1800319475328460017685 1800319475328460017685 No School of Electrical Engineering, Iran University of Science and Technology S. Tohidi s_tohidi@alumni.iust.ac.ir 1800319475328460017686 1800319475328460017686 No School of Electrical Engineering, Iran University of Science and Technology Mohammad Reza Mosavi m_mosavi@iust.ac.ir 1800319475328460017687 1800319475328460017687 Yes Department of Electrical Engineering, Iran University of Science and Technology Diego Martín De Andrés diego.martin.andres@uva.es 1800319475328460017688 1800319475328460017688 No Department of Computer Science, Universidad de Valladolid