Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs
Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/2/662 |
| _version_ | 1827662798358839296 |
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| author | Tala Talaei Khoei Shereen Ismail Naima Kaabouch |
| author_facet | Tala Talaei Khoei Shereen Ismail Naima Kaabouch |
| author_sort | Tala Talaei Khoei |
| collection | DOAJ |
| description | Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s. |
| first_indexed | 2024-03-10T00:32:59Z |
| format | Article |
| id | doaj.art-e96f73b9be844d789f6eacd962b1bfb1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:32:59Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e96f73b9be844d789f6eacd962b1bfb12023-11-23T15:22:23ZengMDPI AGSensors1424-82202022-01-0122266210.3390/s22020662Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVsTala Talaei Khoei0Shereen Ismail1Naima Kaabouch2School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USAUnmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.https://www.mdpi.com/1424-8220/22/2/662unmanned aerial vehiclesglobal positioning systemGPS spoofing attacksdetection techniquesmachine learningdynamic selection |
| spellingShingle | Tala Talaei Khoei Shereen Ismail Naima Kaabouch Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs Sensors unmanned aerial vehicles global positioning system GPS spoofing attacks detection techniques machine learning dynamic selection |
| title | Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs |
| title_full | Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs |
| title_fullStr | Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs |
| title_full_unstemmed | Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs |
| title_short | Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs |
| title_sort | dynamic selection techniques for detecting gps spoofing attacks on uavs |
| topic | unmanned aerial vehicles global positioning system GPS spoofing attacks detection techniques machine learning dynamic selection |
| url | https://www.mdpi.com/1424-8220/22/2/662 |
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