Assessment of Multi-Model Approach for Drone Security

Drones, while critical for numerous applications, are particularly susceptible to a variety of cyber threats. Tra-ditional single-model security solutions often present inherent weaknesses, creating specific attack surfaces that can be exploited by adversaries. This paper explores a multi-model approach to drone security, addressing these vulnerabilities through system diversity. By integrating a range of models, each leveraging different modalities such as sensor data and computer vision and training them on a combination of real-world inputs and synthetically generated images within simulated environments, we propose a more robust and adaptive security framework. This approach is designed to improve threat detection capabilities and overall system resilience, enabling drones to better counter and adapt to evolving cyber attacks.