Evaluation of Machine Learning Models for Attack Detection in Unmanned Aerial Vehicle Networks

Year 2024, Volume: 16 Issue: 2, 400 - 410, 31.12.2024

Ahmet Faruk Görmüş , Serkan Gönen , Abdulsamet Haşıloğlu , Ercan Nurcan Yılmaz

https://doi.org/10.47000/tjmcs.1568820

Abstract

Nowadays, unmanned aerial vehicles (UAVs) are increasingly utilized in various civil and military
applications, highlighting the growing need for robust security in UAV networks. Cyberattacks on these networks can lead to operational disruptions and the loss of critical information. This study evaluates five machine learning models—Random Forest (RF), CatBoost, XGBoost, AdaBoost, and Artificial Neural Networks (ANN)—for detecting attacks on UAV networks using the CICIOT2023 (Canadian Institute for Cybersecurity Internet of Things 2023) dataset. Performance metrics such as accuracy, precision, sensitivity, and F1 score were used to assess these models. Among them, CatBoost demonstrated superior performance, achieving the highest accuracy and the fastest prediction time of 6.487 seconds, making it particularly advantageous for real-time attack detection. This study underscores the effectiveness of CatBoost in both accuracy and efficiency, positioning it as an ideal choice for enhancing UAV network security. The findings contribute to addressing cybersecurity vulnerabilities in UAV networks and support the development of more secure network infrastructures.

Keywords

UAV networks, cyber attack, attack detection, CICIOT2023 dataset, performance evaluation, security vulnerabilities.

References

• Derhab, A., Cheikhrouhou, O., Allouch, A., Koubaa, A., Qureshi, B. et al., Internet of drones security: Taxonomies, open issues, and future directions, Vehicular Communications 39(2023), 100552.
• Durfey, N., Sajal, S., A comprehensive survey: Cybersecurity challenges and futures of autonomous drones, 2022 Intermountain Engineering, Technology and Computing (IETC), (2022), 1–7.
• Gabrielsson, J., Bugeja, J., Vogel, B., Hacking a commercial drone with open-source software: Exploring data privacy violations, 2021 10th mediterranean conference on embedded computing (MECO), IEEE, 2(021), 1–5.
• Galvan, J., Raja, A., Li, Y., Yuan, J., Sensor data-driven uav anomaly detection using deep learning approach, MILCOM 2021-2021 IEEE Military Communications Conference (MILCOM), IEEE, (2021), 589–594.
• Jony, A.I., Arnob, A.K.B., A long short-term memory based approach for detecting cyber attacks in iot using cic-iot2023 dataset, Journal of Edge Computing 3(1)(2024), 28–42.
• Nayak, J., Naik, B., Dash, P.B., Vimal, S., Kadry, S., Hybrid bayesian optimization hypertuned catboost approach for malicious access and anomaly detection in iot nomalyframework, Sustainable Computing: Informatics and Systems, 36(2022), 100805.
• Neto, E.C.P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R. et al., Ciciot2023: A real-time dataset and benchmark for large-scale attacks in iot environment, Sensors, 23(13)(2023), 5941.
• Noorwali, A., Javed, M.A., Khan, M.Z., Efficient uav communications: Recent trends and challenges, Computers, Materials & Continua, 67(1)(2021).
• Ahmad, W., Almaiah, M.A., Ali, A., Al-Shareeda, M.A., Deep learning based network intrusion detection for unmanned aerial vehicle (uav), 2024 7th World Conference on Computing and Communication Technologies (WCCCT), IEEE, (2024), 31–36.
• Alqahtani, H., Kumar, G., Machine learning for enhancing transportation security: A comprehensive analysis of electric and flying vehicle systems, Engineering Applications of Artificial Intelligence 129(2024), 107667.
• Alrefaei, F., Machine learning for intrusion detection into unmanned aerial system 6g networks, Doctoral dissertation, Embry-Riddle Aeronautical University, (2024).
• Miao, S., Pan, Q., Zheng, D., Unmanned aerial vehicle intrusion detection: Deep-meta-heuristic system, Vehicular Communications, 46(2024),100726.
• Hadi, H.J., Cao, Y., Li, S., Hu, Y., Wang, J. et al., Real-time collaborative intrusion detection system in uav networks using deep learning, IEEE Internet of Things Journal, (2024).
• Khoei, T.T., Al Shamaileh, K., Devabhaktuni, V. K., Kaabouch, N., A comparative assessment of unsupervised deep learning models for detecting gps spoofing attacks on unmanned aerial systems, 2024 Integrated Communications, Navigation and Surveillance Conference (ICNS), IEEE, (2024), 1–10.
• Wu, Y., Yang, L., Zhang, L., Nie, L., Zheng, L., Intrusion detection for unmanned aerial vehicles security: A tiny machine learning model, IEEE Internet of Things Journal, 11(2024), 20970–20980.
• Al-lQubaydhi, N., Alenezi, A., Alanazi, T., Senyor, A., Alanezi, N. et al., Deep learning for unmanned aerial vehicles detection: A review, Computer Science Review, 51(2024), 100614.
• Mynuddin, M., Khan, S.U., Ahmari, R., Landivar, L., Mahmoud, M.N. et al., Trojan attack and defense for deep learning based navigation systems of unmanned aerial vehicles, IEEE Access, 12(2024), 89887–89897
• Omolara, A.E., Alawida, M., Abiodun, O.I., Drone cybersecurity issues, solutions, trend insights and future perspectives: A survey, Neural Computing and Applications, 35(31)(2023), 23063–23101.
• Sharifani, K., Amini, M., Machine learning and deep learning: A review of methods and applications, World Information Technology and Engineering Journal, 10(07)(2023), 3897–3904.
• Yahuza, M., Idris, M.Y.I., Ahmedy, I.B., Wahab, A.W.A., Nandy, T. et al., Internet of drones security and privacy issues: Taxonomy and open challenges, IEEE Access 9(2021), 57243–57270.