The week's pick
Reseach Article
Development of a Distributed Denial of Service Attack Detection Scheme for Multi-UAV Network
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 47 |
| Year of Publication: 2025 |
| Authors: Oluwaseun Adesola-Zion , Kazeem B. Adedeji |
10.5120/ijais2025452017
|
Oluwaseun Adesola-Zion , Kazeem B. Adedeji . Development of a Distributed Denial of Service Attack Detection Scheme for Multi-UAV Network. International Journal of Applied Information Systems. 12, 47 ( Mar 2025), 16-24. DOI=10.5120/ijais2025452017
Abstract
Unmanned Aerial Vehicle (UAV) networks are susceptible to several cyber attack due to the broadcast nature of the wireless communication architecture between the UAV and the ground station. Among these threats, Distributed Denial-of-Service (DDoS) attacks pose significant risks to UAV networks. This study develops an effective machine learning-based scheme for detecting DDoS attacks in UAV networks. A comprehensive, labeled network traffic dataset, encompassing both normal and malicious traffic, was curated and preprocessed through normalization and the removal of missing values. Three ensemble classifiers were developed for attack detection. Classifier 1 combines Logistic Regression (LR) and Decision Tree (DT), Classifier 2 integrates Random Forest (RF) and DT and Classifier 3 leverages a hybrid of LR, DT, and RF. The classifiers were trained and evaluated using a dataset split into 70% training, 10% validation, and 20% test subsets. Feature extraction technique was employed to identify key characteristics of network traffic essential for detecting attack patterns. The classifiers' performance was assessed using metrics such as accuracy, precision, recall, F1-score, ROC curve, loss function, and epoch analysis. Results showed that Classifier 2 achieved the best performance, with 97.05% accuracy, 98.79% precision, and a 97.27% F1-score, demonstrating its robustness in detecting DDoS attacks. Classifier 3 exhibited comparable performance, with 97.09% accuracy, 97.41% precision, and 97.34% F1-score, but a slightly higher loss value, making it slightly less robust. Classifier 1, while achieving reasonable accuracy (87.68%) and precision (97.99%), showed weaker recall (79.17%) and F1-score (87.58%), indicating limited reliability. This study has shown that the detection accuracy of DDoS attack in UAV networks can be improved with the use of ensemble-based methodology.
References
- Chandran, I. and Vipin, K., 2024. Multi-UAV networks for disaster monitoring: challenges and opportunities from a network perspective. Drone Systems and Applications, 12, 1-28.
- Hayat, S., Yanmaz, E. and Muzaffar, R., 2016. Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint. IEEE Communications Surveys & Tutorials, 18(4), 2624-2661.
- Javaid, S., Saeed, N., Qadir, Z., Fahim, H., He, B., Song, H. and Bilal, M., 2023. Communication and control in collaborative UAVs: Recent advances and future trends. IEEE Transactions on Intelligent Transportation Systems, 24(6), 5719-5739.
- Mairaj, A. and Javaid, A.Y., 2022. Game theoretic solution for an Unmanned Aerial Vehicle network host under DDoS attack. Computer networks, 211, 1-24.
- Branco, B., Silva, J.S. and Correia, M., 2025. Cyber Attacks on Commercial Drones: A Review. IEEE Access, 13, 9566-9577.
- Adedeji, K.B., Abu-Mahfouz, A.M. and Kurien, A.M., 2023. DDoS attack and detection methods in internet-enabled networks: Concept, research perspectives, and challenges. Journal of Sensor and Actuator Networks, 12(4), 1-51.
- Rabah, M.A.O., Drid, H., Medjadba, Y. and Rahouti, M., 2024. Detection and Mitigation of Distributed Denial of Service Attacks Using Ensemble Learning and Honeypots in a Novel SDN-UAV Network Architecture. IEEE Access, 12, 128929-128940.
- Adedeji, K.B., Oladiran, S.O., Abokede, S.V. and Ogunlade, O. 2024.Prospect of machine learning scheme for efficient detection of DDoS attacks in IoT networks. Journal of Multidisciplinary Engineering Science Studies, 10(11), 5648-5658.
- Carlo, A. and Obergfaell, K., 2024. Cyber attacks on critical infrastructures and satellite communications. International Journal of Critical Infrastructure Protection, 46, 100701.
- Son, S.B. and Kim, D.H., 2023. Searching for Scalable Networks in Unmanned Aerial Vehicle Infrastructure Using Spatio-Attack Course-of-Action. Drones, 7(4), 1-15.
- Chamola, V., Kotesh, P., Agarwal, A., Gupta, N. and Guizani, M. 2021. A comprehensive review of unmanned aerial vehicle attacks and neutralization techniques. Ad Hoc Networks, 111, 102324.
- Pirayesh, H. and Zeng, H. 2022. Jamming attacks and anti-jamming strategies in wireless networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 1–40.
- Geraci, G., Garcia-Rodriguez, A., Azari, M.M., Lozano, A., Mezzavilla, M., Chatzinotas, S. and Di Renzo, M. 2022. What will the future of UAV cellular communications be? A flight from 5G to 6G. IEEE Communications Surveys and Tutorials, 24(3), 1304-1335.
- Mynuddin, M., Khan, S.U., Ahmari, R., Landivar, L., Mahmoud, M.N. and Homaifar, A., 2024. Trojan attack and defense for deep learning based navigation systems of unmanned aerial vehicles. IEEE Access, 12, 89887-89907.
- Wang, X., Zhao, Z., Yi, L., Ning, Z., Guo, L., Yu, F.R. and Guo, S., 2024. A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures. ACM Computing Surveys, 57(3), 1-37.
- Riggs, H., Tufail, S., Parvez, I., Tariq, M., Khan, A., Amir, A. and Sarwat, A.I. 2023. Impact, vulnerabilities, and mitigation strategies for cyber-secure critical infrastructure. Sensors, 23(8), 4060.
- Guo, W., Zhang, Z., Chang, L., Song, Y. and Yin, L., 2024. A ddos tracking scheme utilizing adaptive beam search with unmanned aerial vehicles in smart grid. Drones, 8(9), 1-19.
- Khan, M. and Ghafoor, L. 2024. Adversarial machine learning in the context of network security: Challenges and solutions. Journal of Computational Intelligence and Robotics, 4(1), 51-63, 2024.
- Mairaj, A., and Javaid, A. Y. 2022. Game theoretic solution for an Unmanned Aerial Vehicle network host under DDoS attack. Computer Networks, 211(4), 108962.
- Shrestha, R., Omidkar, A., Roudi, S. A., Abbas, R., and Kim, S. 2021. Machine-learning- enabled intrusion detection system for cellular connected UAV networks. Electronics, 10(13), 1549.
- Malik, M., Sharma, S., Uddin, M., Chen, C. L., Wu, C. M., Soni, P., and Chaudhary, S. 2022. Waste classification for sustainable development using image recognition with deep learning neural network models. Sustainability, 14(12), 7222.
- Saghezchi, F. B., Mantas, G., Violas, M. A., de Oliveira Duarte, A. M., and Rodriguez, J. 2022. Machine learning for DDoS attack detection in Industry 4.0 CPPSs. Electronics, 11(4), 602.
- Giannaros, A., Karras, A., Theodorakopoulos, L., Karras, C., Kranias, P., Schizas, N. and Tsolis, D. 2023. Autonomous vehicles: sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions. Journal of Cybersecurity and Privacy, 3(3), 493-543.
- Bhayo, J., Shah, S. A., Hameed, S., Ahmed, A., Nasir, J., and Draheim, D. 2023. Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks. Engineering Applications of Artificial Intelligence, 123, 106432.
- Shieh, C. S., Lin, W. W., Nguyen, T. T., Chen, C. H., Horng, M. F., and Miu, D. 2021. Detection of unknown DDoS attacks with deep learning and gaussian mixture model. Applied Sciences, 11(11), 5213.
- Akhtar, M.S. and Feng, T. 2021. Deep learning-based framework for the detection of cyberattack using feature engineering. Security and Communication Networks, 2021(1), 1-12.
- Shrestha, R., Omidkar, A., Roudi, S. A., Abbas, R., and Kim, S. 2021. Machine-learning- enabled intrusion detection system for cellular connected UAV networks. Electronics. 10(13), 1549.
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