DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural network
The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools...
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-10-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864821000663 |
| _version_ | 1811313904185245696 |
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| author | Peng Jiang Hongyi Wu Chunsheng Xin |
| author_facet | Peng Jiang Hongyi Wu Chunsheng Xin |
| author_sort | Peng Jiang |
| collection | DOAJ |
| description | The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this end, we propose DeepPOSE, a deep learning model, to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms. Our design uses a convolutional and recurrent neural network to reduce the noise, to recover a vehicle's real-time trajectory from multiple sensor inputs. We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map, to smartly eliminate the accumulation of errors on the trajectory estimation. The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack. Compared with the existing method, the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks. |
| first_indexed | 2024-04-13T11:02:53Z |
| format | Article |
| id | doaj.art-0dc3c60a63b4453a813977989bedb411 |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-04-13T11:02:53Z |
| publishDate | 2022-10-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-0dc3c60a63b4453a813977989bedb4112022-12-22T02:49:20ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482022-10-0185791803DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural networkPeng Jiang0Hongyi Wu1Chunsheng Xin2ECE Dept. and School of Cybersecurity, Old Dominion University, Norfolk, VA, 23529, USAECE Dept. and School of Cybersecurity, Old Dominion University, Norfolk, VA, 23529, USACorresponding author.; ECE Dept. and School of Cybersecurity, Old Dominion University, Norfolk, VA, 23529, USAThe Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this end, we propose DeepPOSE, a deep learning model, to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms. Our design uses a convolutional and recurrent neural network to reduce the noise, to recover a vehicle's real-time trajectory from multiple sensor inputs. We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map, to smartly eliminate the accumulation of errors on the trajectory estimation. The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack. Compared with the existing method, the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks.http://www.sciencedirect.com/science/article/pii/S2352864821000663GPS spoofing attackPosition estimationRecurrent neural network |
| spellingShingle | Peng Jiang Hongyi Wu Chunsheng Xin DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural network Digital Communications and Networks GPS spoofing attack Position estimation Recurrent neural network |
| title | DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural network |
| title_full | DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural network |
| title_fullStr | DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural network |
| title_full_unstemmed | DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural network |
| title_short | DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural network |
| title_sort | deeppose detecting gps spoofing attack via deep recurrent neural network |
| topic | GPS spoofing attack Position estimation Recurrent neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2352864821000663 |
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