ExplainabilitySecurityTransparencycs.LG
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
March 25, 2024
International Conference on Computing, Networking and Communications
22 citations
6 influential
Key Takeaways
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Zero Trust Architecture is essential for UAV security: Rather than relying on traditional perimeter defenses, the paper demonstrates that UAVs require continuous authentication of all network entities and communications to prevent vulnerabilities and unauthorized access.
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Deep learning can identify UAVs with 84.59% accuracy using radio signals: The study shows that analyzing Radio Frequency (RF) signals through deep learning provides a practical method for detecting and identifying UAVs, which is critical for determining whether devices should be granted network access.
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Explainability tools make AI security decisions trustworthy: By using SHAP and LIME—tools that explain how AI models make decisions—the research shows that security classifications become verifiable and understandable to humans, rather than operating as "black boxes," which is important for responsible deployment of AI in security systems.
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Transparent AI improves security accountability: The paper emphasizes that when AI systems used for UAV security are interpretable, their classifications can be audited and verified, creating better accountability and reducing the risk of flawed or biased security decisions affecting legitimate users.
Abstract
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.