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A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms
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Resumo(s)
Deep Neural Networks (DNNs) have gained prominence due to their remarkable accomplishments across various domains, including telecommunications and security. Their integration into decision-making processes within 5G telecommunication systems and UAV security is noteworthy. However, the iterative nature of DNN data processing can introduce uncertainties in classification decisions, impacting their reliability. This paper presents novel combined preprocessing and post-processing techniques designed to enhance the accuracy and reliability of binary classification DNNs by managing uncertainty levels. The study evaluates these methods through calibration error metrics, confidence values, and the Reliability Score (RS), which quantifies the disparity between Mean Accuracy (MA) and Mean Confidence (MC). Additionally, the effectiveness of these methods is demonstrated by applying them to simulated real-world scenarios to improve jamming detection reliability in UAV communications. The proposed algorithms' impact is compared against baseline DNNs and DNNs augmented with the eXtreme Gradient Boosting (XGB) classifier, as well as the latest research to validate our approach. This paper comprehensively overviews the experimental setup, dataset, deep network architecture, preprocessing and post-processing techniques, evaluation metrics, and results. By addressing uncertainty in XGB and DNN outputs, this study improves the trustworthiness of ML-DNN-based decision-making processes in 5G UAV security scenarios.
Descrição
Funding Information:
This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie under Project 813391; in part by the Ministerio de Asuntos Económicos y Transformación Digital (MINECO), in part by the European Union (EU)-NextGenerationEU in the Frameworks of the ‘‘Plan de Recuperación, Transformación y Resiliencia’’ and of the ‘‘Mecanismo de Recuperación y Resiliencia’’ under Grant TSI-063000-2021-55 and Grant PID2021-126431OB-I00; in part by the Ministerio de Ciencia, Innovación y Universidades (MCIN)/Agencia Española de Investigación (AEI)/10.13039/501100011033; in part by the ‘‘European Regional Development Fund (ERDF) A way of making Europe’’ and Generalitat de Catalunya under Grant 2021 SGR 00770; in part by Project ‘‘SOFIA-AIR’’ PID2023-147305OB-C31, Ministerio de Ciencia, Innovación y Universidades (MICIU)/AEI/10.13039/501100011033/Ministerio de Ciencia, Innovación y Universidades (FEDER) EU, in part by FCT—Fundação para a Ciência e Tecnologia, I.P., and Instituto de Telecomunicações under Project UIDB/50008/2020, with DOI identifier https://doi.org/10.54499/UIDB/50008/2020; and in part by the Distributed Access Design for Cell-less Smart 6G Networks (CELL-LESS6G) project, 2022. 08786.PTDC with DOI identifier https://doi.org/10.54499/2022.08786.PTDC.
Publisher Copyright:
© 2013 IEEE.
Palavras-chave
5G 6G deep neural networks eXtreme gradient boosting (XGB) classifier jamming identification machine learning reliability uncertainty Unmanned aerial vehicle General Computer Science General Materials Science General Engineering