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Lecturer, Artificial Intelligence Research Center, Northern Technical University, Mosul, Iraq
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Assistant Professor, Department of Artificial Intelligence Engineering Technology, Northern Technical University, Mosul, Iraq
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Lecturer, Department of Cyber Security Engineering Technology, Northern Technical University, Mosul, Iraq
Professor, Department of Electrical Engineering, College of Engineering, Wasit University, Wasit, Iraq
Unmanned Aerial Vehicle (UAV) networks are being used in various military, civilian and infrastructure-monitoring fields, making them vulnerable to an increasing number of cyber threats. To achieve strong security in these resource-limited, dynamic environments, lightweight and accurate intrusion detection systems (IDS) are necessary. In this paper, three Machine Learning (ML) classifiers: Logistic Regression, Random Forest, and Light Gradient Boosting Machine (LightGBM) and three Deep Learning (DL) architectures: Feed Forward Neural Network (FFNN), one-dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM), are compared to find the best performance for intrusion detection in UAV networks. An 80:20 train-test split was used for training and evaluation, with the same 80:20 ratio applied to both of the two recently released, publicly available benchmark datasets, UAVIDS-2025 and UAV-GCS-IDS, and with weighted-average Accuracy, Precision, Recall, and F1-score as the evaluation metrics. The performance of the models shows that LightGBM has the highest overall performance with 100.0% accuracy and precision on UAVIDS-2025 and 99.04% and 98.93% accuracy and F1 score on UAV-GCS-IDS, outperforming Random Forest with 99.97% and 88.89% accuracy on UAVIDS-2025 and UAV-GCS-IDS, respectively, and the deep learning models with accuracy ranging from 88.2% to 99.6% on UAVIDS-2025 and 89.6% to 98.93% on UAV-GCS-IDS. The Logistic Regression always yielded the lowest scores with only 86.8%-87.0% accuracy in UAVIDS-2025 and around 85.1% in UAV-GCS-IDS, showing the capabilities of a linear decision boundary in these traffic patterns. The results show that when computational resources are available, deep learning architectures are also effective at learning complex, non-linear attack patterns, while gradient-boosted ensemble methods provide the best detection accuracy with the lowest computational burden for UAV intrusion detection. Finally, the paper outlines recommended deployment strategies for ensembles and hybrid ML-DL, which are applicable in the context of resource-constrained environments of UAV networks.
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