A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms
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 na...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10763503/ |
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| author | Hamed Farkhari Joseanne Viana Sarang Kahvazadeh Pedro Sebastiao Victor P. Gil Jimenez Rui Dinis |
| author_facet | Hamed Farkhari Joseanne Viana Sarang Kahvazadeh Pedro Sebastiao Victor P. Gil Jimenez Rui Dinis |
| author_sort | Hamed Farkhari |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a95c718f1756462492fa5ff15eeaa169 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a95c718f1756462492fa5ff15eeaa1692024-12-11T00:05:20ZengIEEEIEEE Access2169-35362024-01-011217889817890810.1109/ACCESS.2024.350472910763503A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML AlgorithmsHamed Farkhari0https://orcid.org/0000-0002-2620-260XJoseanne Viana1https://orcid.org/0000-0002-4191-3127Sarang Kahvazadeh2https://orcid.org/0000-0001-5607-8120Pedro Sebastiao3https://orcid.org/0000-0001-7729-4033Victor P. Gil Jimenez4https://orcid.org/0000-0001-7029-1710Rui Dinis5https://orcid.org/0000-0002-8520-7267ISCTE-Instituto Universitário de Lisboa, Lisbon, PortugalDepartamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid (UC3M), Madrid, SpainCentre Tecnològic de Telecomunicacions de Catalunya (CTTC), CERCA, Barcelona, SpainISCTE-Instituto Universitário de Lisboa, Lisbon, PortugalDepartamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid (UC3M), Madrid, SpainInstituto de Telecomunicações (IT), Lisbon, PortugalDeep 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.https://ieeexplore.ieee.org/document/10763503/Unmanned aerial vehicledeep neural networksmachine learninguncertaintyreliabilityjamming identification |
| spellingShingle | Hamed Farkhari Joseanne Viana Sarang Kahvazadeh Pedro Sebastiao Victor P. Gil Jimenez Rui Dinis A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms IEEE Access Unmanned aerial vehicle deep neural networks machine learning uncertainty reliability jamming identification |
| title | A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms |
| title_full | A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms |
| title_fullStr | A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms |
| title_full_unstemmed | A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms |
| title_short | A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms |
| title_sort | hybrid approach to reliable jamming identification in uav communications using combined dnns and ml algorithms |
| topic | Unmanned aerial vehicle deep neural networks machine learning uncertainty reliability jamming identification |
| url | https://ieeexplore.ieee.org/document/10763503/ |
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