Detection of DDoS Attacks in UAV Communication Networks Using Machine Learning Models

Abstract

The increasing adoption of unmanned aerial vehicle (UAV) communication networks has introduced new cybersecurity challenges, particularly in detecting and mitigating distributed denial-of-service (DDoS) attacks. This study evaluates the effectiveness of multiple machine learning models, including Random Forest, Gradient Boosting, XGBoost, Logistic Regression, and Support Vector Machine (SVM), for DDoS attack detection in UAV networks. The dataset, derived from a simulated UAV communication network, incorporates key network parameters such as signal strength, packet loss rate, round-trip time, and base station load. Data preprocessing steps, including feature selection, normalization, and synthetic minority over-sampling (SMOTE), were applied to enhance model performance. Among the evaluated models, Random Forest demonstrated the highest classification accuracy with an F1-score of 0.839 and an AUC score of 0.912, outperforming other models in precision-recall trade-offs. Gradient Boosting and XGBoost exhibited moderate classification ability, whereas Logistic Regression and SVM struggled with capturing complex network patterns. The results highlight the effectiveness of ensemble learning in intrusion detection for UAV networks. This study provides valuable insights into optimizing machine learning-based intrusion detection systems and paves the way for further advancements in UAV cybersecurity. Future work will focus on integrating additional feature engineering techniques and validating models on real-time network traffic datasets.