MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS

Feyzioglu, Ahmet; TASPINAR, YAVUZ SELIM

doi:10.46519/ij3dptdi.1268605

MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS

Ahmet FEYZİOĞLU, (Marmara Üniversitesi, Teknoloji Fakültesi, Makine Mühendisliği Bölümü, İstanbul, Türkiye)

Yavuz Selim TASPINAR (Selçuk Üniversitesi, Doğanhisar Meslek Yüksekokulu, Ulaştırma ve Trafik Hizmetleri, Bölümü, Konya, Türkiye)

International Journal of 3D Printing Technologies and Digital Industry

5 3

Yıl: 2023 Cilt: 7 Sayı: 2 Sayfa Aralığı: 277 - 285 Metin Dili: İngilizce DOI: 10.46519/ij3dptdi.1268605 İndeks Tarihi: 07-09-2023

MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS

Öz:

Aircraft are used in many fields such as engineering, logistics, transportation and disaster management. With the development of drones, aerial vehicles have become more widely used for entertainment purposes. However, in addition to its useful applications, its malicious use is also becoming widespread. It has become a necessity to eliminate this problem, especially since it poses a significant danger to other aircraft. In order to identify the aircraft and solve this problem quickly, in this study, five different aircraft were classified based on images. In the study, a five-class dataset containing aeroplane, bird, drone, helicopter and malicious UAV (Unnamed Aerial Vehicle) images was used. Three different CNN (Convolutional Neural Network) models were employed to extract the images of features. Image features extracted with SqueezeNet, VGG16, VGG19 models were classified with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR) machine learning methods. As a result of the experiments, the most accuracyful result, 92%, was obtained from the classification of the features extracted with the SqueezeNet model with ANN. The models proposed in the study will be integrated into various systems and used in the field of aviation to detect malicious UAVs and take necessary precautions.

Anahtar Kelime: Drone UAV Aeroplane CNN Machine Learning

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	Feyzioglu A, TASPINAR Y (2023). MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. , 277 - 285. 10.46519/ij3dptdi.1268605
Chicago	Feyzioglu Ahmet,TASPINAR YAVUZ SELIM MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. (2023): 277 - 285. 10.46519/ij3dptdi.1268605
MLA	Feyzioglu Ahmet,TASPINAR YAVUZ SELIM MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. , 2023, ss.277 - 285. 10.46519/ij3dptdi.1268605
AMA	Feyzioglu A,TASPINAR Y MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. . 2023; 277 - 285. 10.46519/ij3dptdi.1268605
Vancouver	Feyzioglu A,TASPINAR Y MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. . 2023; 277 - 285. 10.46519/ij3dptdi.1268605
IEEE	Feyzioglu A,TASPINAR Y "MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS." , ss.277 - 285, 2023. 10.46519/ij3dptdi.1268605
ISNAD	Feyzioglu, Ahmet - TASPINAR, YAVUZ SELIM. "MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS". (2023), 277-285. https://doi.org/10.46519/ij3dptdi.1268605

APA	Feyzioglu A, TASPINAR Y (2023). MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. International Journal of 3D Printing Technologies and Digital Industry, 7(2), 277 - 285. 10.46519/ij3dptdi.1268605
Chicago	Feyzioglu Ahmet,TASPINAR YAVUZ SELIM MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. International Journal of 3D Printing Technologies and Digital Industry 7, no.2 (2023): 277 - 285. 10.46519/ij3dptdi.1268605
MLA	Feyzioglu Ahmet,TASPINAR YAVUZ SELIM MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. International Journal of 3D Printing Technologies and Digital Industry, vol.7, no.2, 2023, ss.277 - 285. 10.46519/ij3dptdi.1268605
AMA	Feyzioglu A,TASPINAR Y MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. International Journal of 3D Printing Technologies and Digital Industry. 2023; 7(2): 277 - 285. 10.46519/ij3dptdi.1268605
Vancouver	Feyzioglu A,TASPINAR Y MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS. International Journal of 3D Printing Technologies and Digital Industry. 2023; 7(2): 277 - 285. 10.46519/ij3dptdi.1268605
IEEE	Feyzioglu A,TASPINAR Y "MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS." International Journal of 3D Printing Technologies and Digital Industry, 7, ss.277 - 285, 2023. 10.46519/ij3dptdi.1268605
ISNAD	Feyzioglu, Ahmet - TASPINAR, YAVUZ SELIM. "MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS". International Journal of 3D Printing Technologies and Digital Industry 7/2 (2023), 277-285. https://doi.org/10.46519/ij3dptdi.1268605