Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep
  Learning and Explainable AI Analysis
Ekramul Haque Kamrul Hasan Imtiaz Ahmed Md. Sahabul Alam Tariqul Islam
In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs),
the utmost importance lies in guaranteeing resilient and lucid security
measures. This study highlights the necessity of implementing a Zero Trust
Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs),
hence departing from conventional perimeter defences that may expose
vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous
and continuous process of authenticating all network entities and
communications. The accuracy of our methodology in detecting and identifying
unmanned aerial vehicles (UAVs) is 84.59\%. This is achieved by utilizing Radio
Frequency (RF) signals within a Deep Learning framework, a unique method.
Precise identification is crucial in Zero Trust Architecture (ZTA), as it
determines network access. In addition, the use of eXplainable Artificial
Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local
Interpretable Model-agnostic Explanations (LIME) contributes to the improvement
of the model's transparency and interpretability. Adherence to Zero Trust
Architecture (ZTA) standards guarantees that the classifications of unmanned
aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security
within the UAV field.