A Comparative of Detecting Physical and Cyber Attacks on Drones Using Machine Learning and Deep Learning Techniques

Authors

Khattab M Ali Alheeti, Department of Computer Networking Systems, College of Computer and Information Technology, University of Anbar, Anbar, IraqFollow
Sara Abbas Rafa, Department of Law, College of law and political science, University of Anbar, Ramadi, IraqFollow
Maha Mahmood, Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, IraqFollow
Aythem Khairi Kareem, Department of Heet Education, General Directorate of Education in Anbar, Ministry of Education, Anbar, IraqFollow
Mohammad Aljanabi, Imam Ja’afar Al-Sadiq University, Baghdad, IraqFollow
Ahmed Adil Nafea, Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, IraqFollow

Abstract

Multitudes of Unmanned Aerial Vehicles (UAVs) are generally embraced in military and civilian applications. Yet, this physical-cyber method is intimidated by cyber-attacks. Recently, Machine Learning (ML) based attacks detection approaches have been effectively embraced to detect cyber-attacks. This paper presented the Intrusion Detection Security (IDS) approach. The proposed approach investigates UAVs' cyber and physical attributes under normal process and attack circumstances. Two types of cyber-attacks have been classified: Denial-of-service (DoS) and replay. This study developed IDS approaches established on ML and Deep Learning (DL) prototypes, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), and One Dimensional Convolutional Neural Networks (1DCNN). The produced approach is trained using physical and cyber attributes individually. The finding results indicate that the 1D-CNN model achieved higher accuracy (99.79%) compared to the machine learning algorithms. The experimental results show the efficiency of the proposed method's performance.

Keywords

Cyber attacks, Deep learning, Internet of Things, Intrusion Detection Security, Machine learning, Physical attacks, Unmanned aerial vehicles

Subject Area

Computer Science

Article Type

Article

First Page

2800

Last Page

2812

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite this Article

Alheeti, Khattab M Ali; Rafa, Sara Abbas; Mahmood, Maha; Kareem, Aythem Khairi; Aljanabi, Mohammad; and Nafea, Ahmed Adil (2025) "A Comparative of Detecting Physical and Cyber Attacks on Drones Using Machine Learning and Deep Learning Techniques," Baghdad Science Journal: Vol. 22: Iss. 8, Article 28.
DOI: https://doi.org/10.21123/2411-7986.5039