Enhancing Drone Security Through Multi-Sensor Anomaly Detection and Machine Learning

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have determined numerous applications across industries, ranging from aerial surveillance to package shipping. As drones are used in vital operations, ensuring cyberattacks and anomalies protect them from attackers is now a big challenge. This research study presents a complete approach to enhancing drone safety by integrating multi-sensor anomaly detection and superior machine learning techniques. The proposed methodology capitalizes on the rich sensor suite embedded in present-day drones, encompassing GPS receivers, accelerometers, gyroscopes, cameras, communication modules, and more. Leveraging an array of sensors in drones, our technique detects abnormal drone behavior indicative of unauthorized access, GPS spoofing, communication jamming, and malicious activities. By extracting features from sensor records, we develop a robust anomaly detection framework using the “uav attack dataset” able to identify deviations from normal flight patterns, communication signals, and environmental interactions. Central to our methodology is the utilization of machine learning algorithms. These algorithms are skilled on labeled datasets containing numerous flight eventualities, each normal and hostile, together with the ones discovered inside the “uav attack dataset”. The obtained results are eventually evaluated using rigorous performance metrics to quantify their effectiveness in distinguishing genuine anomalies from benign variations. The findings of our study underscore the capacity of multi-sensor anomaly detection for drones. By harnessing the power of machine learning and sensor fusion, we exhibit the ability to hit upon attacks at an early level, mitigating capability harm and permitting rapid responses. This study contributes now not only to the field of drone safety but also to the broader panorama of self-sustaining systems protection, highlighting the importance of adaptive and proactive protection mechanisms. Results show that an accuracy of 99