en 5-Signal Processing Signal Processing Research Paper Research Paper <span style="font-size:10pt"><span new="" roman="" style="font-family:" times=""><a name="_Hlk169581224">Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.</a></span></span> GPS, Spoofing Detection, CAF, TCNN, Dimension Reduction Algorithm 41 54 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-78-34&slc_lang=en&sid=1 M. J. Jahantab Jahantab_m77@elec.iust.ac.ir 1800319475328460014891 1800319475328460014891 No School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. S. Tohidi s_tohidi@elec.iust.ac.ir 1800319475328460014892 1800319475328460014892 No School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. Mohammad Reza Mosavi m_mosavi@iust.ac.ir 1800319475328460014893 1800319475328460014893 Yes School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. Ahmad Ayatollahi Ayatollahi@iust.ac.ir 1800319475328460014894 1800319475328460014894 No School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran.