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

Yıl 2023, Cilt: 7 Sayı: 2, 277 - 285, 31.08.2023

Ahmet Feyzioğlu Yavuz Selim Taspınar

https://doi.org/10.46519/ij3dptdi.1268605

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 Kelimeler

Drone, UAV, Aeroplane, CNN, Machine Learning

Kaynakça

• 1. Shi, X., C. Yang, W. Xie, C. Liang, Z. Shi, and J. Chen, “Anti-drone system with multiple surveillance technologies: Architecture, implementation, and challenges”, IEEE Communications Magazine, Vol 56, Issue 4, Pages 68-74, 2018.
• 2. Solodov, A., A. Williams, S. Al Hanaei, and B. Goddard, “Analyzing the threat of unmanned aerial vehicles (UAV) to nuclear facilities”, Security Journal, Vol. 31, Issue 1, Pages 305-324, 2018.
• 3. Ye, D.H., J. Li, Q. Chen, J. Wachs, and C. Bouman, “Deep learning for moving object detection and tracking from a single camera in unmanned aerial vehicles (UAVs)”, Electronic Imaging, Vol. 2018, Issue 10, Pages 466-1-466-6, 2018.
• 4. Basak, S., S. Rajendran, S. Pollin, and B. Scheers, “Combined RF-based drone detection and classification”, IEEE Transactions on Cognitive Communications and Networking, Vol. 8, Issue 1, Pages 111-120, 2021.
• 5. Mezei, J., V. Fiaska, and A. Molnár, “Drone sound detection”, in 2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), IEEE, 2015.
• 6. Drozdowicz, J., M. Wielgo, P. Samczynski, K. Kulpa, J. Krzonkalla, M. Mordzonek, M. Bryl, and Z. Jakielaszek, “35 GHz FMCW drone detection system”, in 2016 17th International Radar Symposium (IRS), IEEE, 2016.
• 7. Taha, B. and A. Shoufan, “Machine learning-based drone detection and classification: State-of-the-art in research”, IEEE access, Pages 138669-138682, 2019.
• 8. Lee, D.-H., “CNN-based single object detection and tracking in videos and its application to drone detection”, Multimedia Tools and Applications, Vol. 80, Issue 26, Pages 34237-34248, 2021.
• 9. Aker, C. and S. Kalkan, “Using deep networks for drone detection”, in 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, 2017.
• 10. Singha, S. and B. Aydin, “Automated Drone Detection Using YOLOv4”, Drones, Vol. 5, Issue 3, Pages 95, 2021.
• 11. Jia, X., Y. Cao, D. O’Connor, J. Zhu, D.C. Tsang, B. Zou, and D. Hou, “Mapping soil pollution by using drone image recognition and machine learning at an arsenic-contaminated agricultural field”, Environmental Pollution, Issue 270, Pages 116281, 2021.
• 12. Kim, B.K., H.-S. Kang, and S.-O. Park, “Drone classification using convolutional neural networks with merged Doppler images”, IEEE Geoscience and Remote Sensing Letters, Vol. 14, Issue 1, Pages 38-42, 2016.
• 13. Mendis, G.J., T. Randeny, J. Wei, and A. Madanayake, “Deep learning based doppler radar for micro UAS detection and classification”, in MILCOM 2016-2016 IEEE Military Communications Conference, IEEE, 2016.
• 14. Rozantsev, A., V. Lepetit, and P. Fua, “Detecting flying objects using a single moving camera”, IEEE transactions on pattern analysis and machine intelligence, Vol. 39, Issue 5, Pages 879-892 , 2016.
• 15. Yoshihashi, R., T.T. Trinh, R. Kawakami, S. You, M. Iida, and T. Naemura, “Differentiating objects by motion: Joint detection and tracking of small flying objects”, arXiv preprint arXiv:1709.04666, 2017.
• 16. Saqib, M., S.D. Khan, N. Sharma, and M. Blumenstein, “A study on detecting drones using deep convolutional neural networks”, in 2017 14th IEEE international conference on advanced video and signal based surveillance (AVSS), IEEE, 2017
• 17. Lee, D., W.G. La, and H. Kim, “Drone detection and identification system using artificial intelligence”, in 2018 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, 2018.
• 18. “Malicious Drone Dataset”, https://www.kaggle.com/datasets/sonainjamil/malicious-drones, October 20, 2022
• 19. Taspinar, Y.S., M. Dogan, I. Cinar, R. Kursun, I.A. Ozkan, and M. Koklu, “Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques”, European Food Research and Technology, Vol. 248, Issue 11, Pages 2707-2725, 2022.
• 20. Taspinar, Y.S. and M. Selek, “Object recognition with hybrid deep learning methods and testing on embedded systems”, International Journal of Intelligent Systems and Applications in Engineering, Vol. 8, Issue 2, Pages 71-77, 2020.
• 21. Unal, Y., Y.S. Taspinar, I. Cinar, R. Kursun, and M. Koklu, “Application of Pre-Trained Deep Convolutional Neural Networks for Coffee Beans Species Detection”, Food Analytical Methods, 2022.
• 22. Kursun, R., I. Cinar, Y.S. Taspinar, and M. Koklu, “Flower Recognition System with Optimized Features for Deep Features”, in 2022 11th Mediterranean Conference on Embedded Computing (MECO). 2022.
• 23. Iandola, F.N., S. Han, M.W. Moskewicz, K. Ashraf, W.J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and<0.5 MB model size”, arXiv preprint arXiv:1602.07360, 2016.
• 24. Simonyan, K. and A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, arXiv preprint arXiv:1409.1556, 2014.
• 25. Singh, D., Y.S. Taspinar, R. Kursun, I. Cinar, M. Koklu, I.A. Ozkan, and H.-N. Lee, “Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models”, Electronics, Vol. 11, Issue 7, Pages 981, 2022.
• 26. Kishore, B., A. Yasar, Y.S. Taspinar, R. Kursun, I. Cinar, V.G. Shankar, M. Koklu, and I. Ofori, “Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction, Computational Intelligence and Neuroscience”, Vol. 2022, Pages 2062944, 2022.
• 27. Koklu, M., R. Kursun, Y.S. Taspinar, and I. Cinar, “Classification of date fruits into genetic varieties using image analysis”, Mathematical Problems in Engineering, 2021.
• 28. Yilmaz, A.B., Y.S. Taspinar, and M. Koklu, “Classification of Malicious Android Applications Using Naive Bayes and Support Vector Machine Algorithms”, International Journal of Intelligent Systems and Applications in Engineering, Vol. 10, Issue 2, Pages 269-274, 2022.
• 29. Koklu, M., I. Cinar, and Y.S. Taspinar, “Classification of rice varieties with deep learning methods”, Computers and electronics in agriculture, Vol. 187, Pages 106285, 2021.
• 30. Ersöz, T. And F. Ersöz, “Data Mining And Machine Learning Approaches In Data Science: Predictive Modeling Of Traffic Accident Causes”, International Journal of 3D Printing Technologies and Digital Industry, Vol. 6, Issue 3, Pages 530-539.
• 31. Çinarer, G., K. Kiliç, And T. Parlar, “A Deep Transfer Learning Framework For The Staging Of Diabetic Retinopathy”, Journal of Scientific Reports-A, Issue 051, Pages 106-119.
• 32. Koklu, M. and Y.S. Taspinar, “Determining the extinguishing status of fuel flames with sound wave by machine learning methods”, İEEE Access, Issue 9, Pages 86207-86216, 2021.
• 33. Taspinar, Y. S., Cinar, I., & Koklu, M., “Prediction of computer type using benchmark scores of hardware units”, Selcuk University Journal of Engineering Sciences, Vol. 20, Issue 1, Pages 11-17.