en 2-Automotive and Consumer Electronics Automotive and Consumer Electronics Research Paper Research Paper <div style="text-align: justify;"><span style="font-family:Times New Roman;"><span style="font-size:14px;"><span style="color:#000000;">The Global Positioning System (GPS) is vulnerable to various deliberate and unintentional interferences. Therefore, identifying and coping with various interferences in this system is essential. This paper analyzes a method of reducing the dimensions of Cross Ambiguity Function (CAF) images in improving the identification of spoofing interference at the GPS using Multi-Layer Perceptron Neural Network (MLP NN) and Convolutional Neural Network (CNN). Using the proposed method reduces data complexity, which can reduce the number of learning data requirements. The simulation results indicate that, by applying the proposed image processing algorithm for different dimensions of CAF images, the CNN performs better than MLP NN in terms of training accuracy; the MLP NN is superior to CNN in terms of convergence speed of training. In addition, the results demonstrate that the operation of the proposed method is appropriate in the case of small-delay spoofed signals. Therefore, for the intervals above 0.25 code chip, the proposed method detects spoofing attacks with a correct detection probability close to one.</span></span></span></div> CAF, GPS, GPS Spoofing Attack, Latent Semantic Analysis, Neural Networks. 2584 2584 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-78-29&slc_lang=en&sid=1 K. Zarrinnegar k_zarrinnegar@elec.iust.ac.ir 1800319475328460011595 1800319475328460011595 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 1800319475328460011596 1800319475328460011596 No School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. M. R. Mosavi m_mosavi@iust.ac.ir 1800319475328460011597 000000022389644X Yes School of Electrical Engineering, Iran University of Science and TechnologySchool of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. A. Sadr sadr@iust.ac.ir 1800319475328460011598 1800319475328460011598 No School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. D. M. de Andrés diego.martin.de.andres@upm.es 1800319475328460011599 1800319475328460011599 No ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain.