Volume 19, Issue 1 (March 2023)                   IJEEE 2023, 19(1): 2584-2584 | Back to browse issues page

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Zarrinnegar K, Tohidi S, Mosavi M R, Sadr A, M. de Andrés D. Improving Cross Ambiguity Function Using Image Processing Approach to Detect GPS Spoofing Attacks. IJEEE 2023; 19 (1) :2584-2584
URL: http://ijeee.iust.ac.ir/article-1-2584-en.html
Improving Cross Ambiguity Function Using Image Processing Approach to Detect GPS Spoofing Attacks
K. Zarrinnegar , S. Tohidi , M. R. Mosavi , A. Sadr , D. M. de Andrés
Abstract:   (1517 Views)
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.
Keywords: CAF , GPS , GPS Spoofing Attack , Latent Semantic Analysis , Neural Networks
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Type of Study: Research Paper | Subject: Automotive and Consumer Electronics
Received: 2022/07/03 | Revised: 2023/02/23 | Accepted: 2022/09/12
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 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.