GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model
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...
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2024-11-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3348-en.pdf |
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| author | M. J. Jahantab S. Tohidi Mohammad Reza Mosavi Ahmad Ayatollahi |
| author_facet | M. J. Jahantab S. Tohidi Mohammad Reza Mosavi Ahmad Ayatollahi |
| author_sort | M. J. Jahantab |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-04394462c09b458595d04237be99b647 |
| institution | Kabale University |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-04394462c09b458595d04237be99b6472025-01-09T18:47:15ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902024-11-012044154GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN ModelM. J. Jahantab0S. Tohidi1Mohammad Reza Mosavi2Ahmad Ayatollahi3 School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. 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.http://ijeee.iust.ac.ir/article-1-3348-en.pdfgpsspoofing detectioncaftcnndimension reduction algorithm |
| spellingShingle | M. J. Jahantab S. Tohidi Mohammad Reza Mosavi Ahmad Ayatollahi GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model Iranian Journal of Electrical and Electronic Engineering gps spoofing detection caf tcnn dimension reduction algorithm |
| title | GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model |
| title_full | GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model |
| title_fullStr | GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model |
| title_full_unstemmed | GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model |
| title_short | GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model |
| title_sort | gps spoofing detection using caf images and neural networks based on the proposed peak mapping dimensionality reduction algorithm and tcnn model |
| topic | gps spoofing detection caf tcnn dimension reduction algorithm |
| url | http://ijeee.iust.ac.ir/article-1-3348-en.pdf |
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