Machine Learning-Based Cyber Attack Detection Framework For Secure Unmanned Aerial Vehicle (UAV) Communication Networks

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are increasingly used in various applications such as surveillance, logistics, environmental monitoring, and disaster management. Despite their numerous benefits, the rapid adoption of UAV systems has introduced significant cybersecurity challenges. UAV communication networks are vulnerable to different types of cyber threats including GPS spoofing, data injection attacks, and network intrusions, which can compromise system functionality, mission objectives, and data security. To address these challenges, this study proposes a machine learning-based framework for detecting cyber attacks in UAV systems. The proposed approach combines supervised and unsupervised learning techniques to analyse UAV telemetry data, communication signals, and operational parameters in real time. By performing behavioural analysis and anomaly detection, the system can identify abnormal patterns and isolate potential cyber threats with high accuracy and minimal false positives. Experimental evaluation demonstrates that the proposed framework can effectively detect various attack scenarios while maintaining efficient response time and reliable performance. The integration of machine learning techniques into UAV cybersecurity systems provides a robust solution for enhancing the safety and reliability of drone communication networks.