Advanced security frameworks for UAV and IoT: A deep learning approach

The integration of unmanned aerial vehicles (UAVs) has opened new avenues for enhanced security and functionality. The security of UAVs through the detection and analysis of unique signal patterns is a critical aspect of this technological advancement. This approach leverages intrinsic signal characteristics to distinguish between UAVs of identical models, providing a robust layer of security at the communication level. The application of artificial intelligence in UAV signal analysis has shown significant potential in improving UAV identification and authentication. Recent advancements utilize deep learning techniques with raw In-phase and Quadrature (I/Q) data to achieve high-precision UAV signal recognition. However, existing deep learning models face challenges with unfamiliar data scenarios involving I/Q data. This work explores alternative transformations of I/Q data and investigates the integration of statistical features such as mean, median, and mode across these transformations. It also evaluates the generalization capability of the proposed methods in various environments and examines the impact of signal-to-noise ratio (SNR) on recognition accuracy. Experimental results underscore the promise of our approach, establishing a solid foundation for practical deep-learning-based UAV security solutions and contributing to the field of IoT.