Machine Learning-Based DDoS Mitigation Framework for Unmanned Aerial Vehicles (UAV) Environment Using Software-Defined Networks (SDN)

Unmanned Aerial Vehicles (UAVs) have become an increasingly important part of modern military operations, surveillance, and disaster response. However, UAV networks are vulnerable to Distributed Denial of Service (DDoS) attacks, which can cause serious disruption to mission-critical operations. In this research, we propose a novel Machine Learning-based DDoS Mitigation Framework for UAV environment using Software-Defined Networks (SDN). The proposed framework utilizes SDN's programmability and centralized control capabilities to provide intelligent traffic management and filtering for UAV networks. Machine learning algorithms are used to analyze network traffic and detect DDoS attacks in real-time. Once an attack is detected, the framework can automatically steer traffic away from the affected network segments, isolate the affected devices, or block the malicious traffic altogether. We used KDDCup to train our machine-learning model. We also compare five machine-learning models (random forest, logistic regression, KNN, decision tree classifier, and XGBoost) to find the most accurate model. Our results show that the proposed framework can effectively mitigate DDoS attacks on UAV networks while maintaining low latency and minimal overhead. Overall, our research presents a novel approach to mitigating the threat of DDoS attacks on UAV networks using SDN and machine learning techniques. The proposed framework can help ensure UAVs' safe and reliable operation in mission-critical scenarios.