GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence
Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/6/1/8 |
| _version_ | 1827665882081394688 |
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| author | Elena Basan Alexandr Basan Alexey Nekrasov Colin Fidge Nikita Sushkin Olga Peskova |
| author_facet | Elena Basan Alexandr Basan Alexey Nekrasov Colin Fidge Nikita Sushkin Olga Peskova |
| author_sort | Elena Basan |
| collection | DOAJ |
| description | Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems. |
| first_indexed | 2024-03-10T01:36:15Z |
| format | Article |
| id | doaj.art-319c7e9624044094bdbad12336ab3ef4 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T01:36:15Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-319c7e9624044094bdbad12336ab3ef42023-11-23T13:31:31ZengMDPI AGDrones2504-446X2021-12-0161810.3390/drones6010008GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler DivergenceElena Basan0Alexandr Basan1Alexey Nekrasov2Colin Fidge3Nikita Sushkin4Olga Peskova5Institute for Computer Technologies and Information Security, Southern Federal University, Chekhova 2, 347922 Taganrog, RussiaInstitute for Computer Technologies and Information Security, Southern Federal University, Chekhova 2, 347922 Taganrog, RussiaInstitute for Computer Technologies and Information Security, Southern Federal University, Chekhova 2, 347922 Taganrog, RussiaFaculty of Science, Gardens Point Campus, Queensland University of Technology (QUT), Brisbane, QLD 4001, AustraliaInstitute for Computer Technologies and Information Security, Southern Federal University, Chekhova 2, 347922 Taganrog, RussiaInstitute for Computer Technologies and Information Security, Southern Federal University, Chekhova 2, 347922 Taganrog, RussiaHere, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems.https://www.mdpi.com/2504-446X/6/1/8UAVGPSvulnerabilitiesanomaliesspoofingKullback–Leibler divergence |
| spellingShingle | Elena Basan Alexandr Basan Alexey Nekrasov Colin Fidge Nikita Sushkin Olga Peskova GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence Drones UAV GPS vulnerabilities anomalies spoofing Kullback–Leibler divergence |
| title | GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence |
| title_full | GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence |
| title_fullStr | GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence |
| title_full_unstemmed | GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence |
| title_short | GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence |
| title_sort | gps spoofing attack detection technology for uavs based on kullback leibler divergence |
| topic | UAV GPS vulnerabilities anomalies spoofing Kullback–Leibler divergence |
| url | https://www.mdpi.com/2504-446X/6/1/8 |
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