Machine Learning Algorithms to Strengthen Cyber Defense Mechanisms in Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have become indispensable across various domains, including military surveillance, disaster management, and commercial applications. However, their increasing reliance on wireless communication and network connectivity makes them vulnerable to a wide range of cyber threats, including denial-of-service attacks, spoofing, and malware intrusions. Traditional security measures, such as cryptographic protocols and rule-based intrusion detection systems, often fall short in mitigating these threats due to their limited adaptability to evolving attack patterns. In this research, we propose an advanced cyber defense framework that leverages machine learning algorithms to detect and mitigate cyber threats in UAV networks. The proposed system integrates multiple machine learning techniques, including Random Forest, Support Vector Machines (SVM), and Neural Networks, to analyze UAV network traffic and identify anomalies indicative of cyberattacks. The model is trained using a comprehensive dataset containing real-world attack scenarios, ensuring robustness in detecting both known and emerging threats. To optimize performance, we employ a feature selection strategy that enhances detection accuracy while reducing computational overhead. The effectiveness of the proposed framework is evaluated through extensive simulations, with performance metrics such as accuracy, precision, recall, and F1-score serving as benchmarks for assessment. Experimental results indicate that the ensemble learning approach significantly improves detection accuracy, outperforming individual classifiers in distinguishing between normal and malicious network traffic. Furthermore, the proposed framework demonstrates high adaptability to new and sophisticated cyber threats, making it a viable solution for strengthening UAV security. This research contributes to the ongoing efforts in securing UAV networks by presenting a scalable and efficient cyber defense mechanism that can be deployed in real-time applications. Future work will focus on optimizing the framework for lightweight deployment in resource-constrained UAV environments and enhancing its detection capabilities through deep learning-based techniques.