Determination Working Modes of Unmanned Aerial Vehicles (UAV) over Encrypted Wi-Fi Traffic using Artificial Neural Networks
Developing technology has also made the Unmanned Aerial Vehicles (UAV) widespread. While UAVs provide beneficial use in many sectors from engineering solutions to visual arts, they also come up with malicious uses and can even be used as a tool for committing crimes. Although the states are trying t...
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| Format: | Article |
| Language: | English |
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Gazi University
2021-09-01
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| Series: | Gazi Üniversitesi Fen Bilimleri Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1914125 |
| _version_ | 1797918686578737152 |
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| author | Cengiz SERTKAYA Osman COŞKUN |
| author_facet | Cengiz SERTKAYA Osman COŞKUN |
| author_sort | Cengiz SERTKAYA |
| collection | DOAJ |
| description | Developing technology has also made the Unmanned Aerial Vehicles (UAV) widespread. While UAVs provide beneficial use in many sectors from engineering solutions to visual arts, they also come up with malicious uses and can even be used as a tool for committing crimes. Although the states are trying to register its use with legislation in order to prevent this problem, the problem has not been completely eliminated. The most important problem we face about UAVs is to be able to percept quickly and effectively for what purpose they are flying over a certain region. Although previous studies in the literature were partially successful in solving this problem, it could not be considered as an effective solution due to high costs and long detection time. In this study, the encrypted wi-fi traffic was tried to be defined by the data packet size analysis method to determine the operating modes of the UAVs. Since the amount of data and data processing speed are the most important factors in the detection of UAVs, processes based on artificial intelligence and machine learning have been applied. Using the feed-forward backpropagation artificial neural network method, the operating modes of the UAVs were determined and a success rate of 99.29% was achieved. |
| first_indexed | 2024-04-10T13:33:29Z |
| format | Article |
| id | doaj.art-6317038a51eb4fc38995c9c82f48d34e |
| institution | Directory Open Access Journal |
| issn | 2147-9526 |
| language | English |
| last_indexed | 2024-04-10T13:33:29Z |
| publishDate | 2021-09-01 |
| publisher | Gazi University |
| record_format | Article |
| series | Gazi Üniversitesi Fen Bilimleri Dergisi |
| spelling | doaj.art-6317038a51eb4fc38995c9c82f48d34e2023-02-15T16:11:28ZengGazi UniversityGazi Üniversitesi Fen Bilimleri Dergisi2147-95262021-09-019356257210.29109/gujsc.980170Determination Working Modes of Unmanned Aerial Vehicles (UAV) over Encrypted Wi-Fi Traffic using Artificial Neural NetworksCengiz SERTKAYAhttps://orcid.org/0000-0002-2802-8297Osman COŞKUNhttps://orcid.org/0000-0002-5916-0573Developing technology has also made the Unmanned Aerial Vehicles (UAV) widespread. While UAVs provide beneficial use in many sectors from engineering solutions to visual arts, they also come up with malicious uses and can even be used as a tool for committing crimes. Although the states are trying to register its use with legislation in order to prevent this problem, the problem has not been completely eliminated. The most important problem we face about UAVs is to be able to percept quickly and effectively for what purpose they are flying over a certain region. Although previous studies in the literature were partially successful in solving this problem, it could not be considered as an effective solution due to high costs and long detection time. In this study, the encrypted wi-fi traffic was tried to be defined by the data packet size analysis method to determine the operating modes of the UAVs. Since the amount of data and data processing speed are the most important factors in the detection of UAVs, processes based on artificial intelligence and machine learning have been applied. Using the feed-forward backpropagation artificial neural network method, the operating modes of the UAVs were determined and a success rate of 99.29% was achieved.https://dergipark.org.tr/tr/download/article-file/1914125uav perceptionencrypted wi-fi trafficartificial neural networks |
| spellingShingle | Cengiz SERTKAYA Osman COŞKUN Determination Working Modes of Unmanned Aerial Vehicles (UAV) over Encrypted Wi-Fi Traffic using Artificial Neural Networks Gazi Üniversitesi Fen Bilimleri Dergisi uav perception encrypted wi-fi traffic artificial neural networks |
| title | Determination Working Modes of Unmanned Aerial Vehicles (UAV) over Encrypted Wi-Fi Traffic using Artificial Neural Networks |
| title_full | Determination Working Modes of Unmanned Aerial Vehicles (UAV) over Encrypted Wi-Fi Traffic using Artificial Neural Networks |
| title_fullStr | Determination Working Modes of Unmanned Aerial Vehicles (UAV) over Encrypted Wi-Fi Traffic using Artificial Neural Networks |
| title_full_unstemmed | Determination Working Modes of Unmanned Aerial Vehicles (UAV) over Encrypted Wi-Fi Traffic using Artificial Neural Networks |
| title_short | Determination Working Modes of Unmanned Aerial Vehicles (UAV) over Encrypted Wi-Fi Traffic using Artificial Neural Networks |
| title_sort | determination working modes of unmanned aerial vehicles uav over encrypted wi fi traffic using artificial neural networks |
| topic | uav perception encrypted wi-fi traffic artificial neural networks |
| url | https://dergipark.org.tr/tr/download/article-file/1914125 |
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