A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms


Por: Farkhari H., Viana J., Kahvazadeh S., Sebastião P., Jimenez V.P.G., Dinis R.

Publicada: 1 ene 2024
Resumen:
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. © 2013 IEEE.

Filiaciones:
Farkhari H.:
 ISCTE-Instituto Universitário de Lisboa, Lisbon, 1649-026, Portugal

Viana J.:
 Universidad Carlos III de Madrid (UC3M), Departamento de Teoría de la Señal y Comunicaciones, Madrid, 28903, Spain

Kahvazadeh S.:
 CERCA, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Barcelona, 08860, Spain

Sebastião P.:
 ISCTE-Instituto Universitário de Lisboa, Lisbon, 1649-026, Portugal

 Instituto de Telecomunicações (IT), Lisbon, 1049-001, Portugal

Jimenez V.P.G.:
 Universidad Carlos III de Madrid (UC3M), Departamento de Teoría de la Señal y Comunicaciones, Madrid, 28903, Spain

Dinis R.:
 Instituto de Telecomunicações (IT), Lisbon, 1049-001, Portugal

 Universidade Nova de Lisboa, Monte da Caparica, FCT, Caparica, 2829-516, Portugal
ISSN: 21693536
Editorial
Institute of Electrical and Electronics Engineers Inc., 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA, Estados Unidos America
Tipo de documento: Article
Volumen: 12 Número:
Páginas: 178898-178908
WOS Id: 001373800700039
imagen gold, All Open Access; Gold Open Access

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