TY - GEN
T1 - Accurate and Reliable Methods for 5G UAV Jamming Identification With Calibrated Uncertainty
AU - Farkhari, Hamed
AU - Viana, Joseanne
AU - Sebastião, Pedro
AU - Bernardo, Luís
AU - Kahvazadeh, Sarang
AU - Dinis, Rui
N1 - Funding Information:
This research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Project Number 813391. Also, this work was partially supported by Fundação para a Ciência e a Tecnologia and Instituto de Telecomunicações under Project UIDB/50008/2020.
Publisher Copyright:
© 2023 CEUR-WS. All rights reserved.
PY - 2023
Y1 - 2023
N2 - This research highlights the negative impact of ignoring uncertainty on DNN decision-making and Reliability. Proposed combined preprocessing and post-processing methods enhance DNN accuracy and Reliability in time-series binary classification for 5G UAV security dataset, employing ML algorithms and confidence values. Several metrics are used to evaluate the proposed hybrid algorithms. The study emphasizes the XGB classifier’s unreliability and suggests the proposed methods’ potential superiority over the DNN softmax layer. Furthermore, improved uncertainty calibration based on the Reliability Score metric minimizes the difference between Mean Confidence and Accuracy, enhancing accuracy and Reliability.
AB - This research highlights the negative impact of ignoring uncertainty on DNN decision-making and Reliability. Proposed combined preprocessing and post-processing methods enhance DNN accuracy and Reliability in time-series binary classification for 5G UAV security dataset, employing ML algorithms and confidence values. Several metrics are used to evaluate the proposed hybrid algorithms. The study emphasizes the XGB classifier’s unreliability and suggests the proposed methods’ potential superiority over the DNN softmax layer. Furthermore, improved uncertainty calibration based on the Reliability Score metric minimizes the difference between Mean Confidence and Accuracy, enhancing accuracy and Reliability.
KW - 5G
KW - 6G
KW - Calibration
KW - Deep Neural Networks
KW - Jamming Identification
KW - Reliability
KW - Uncertainty
KW - Unmanned Aerial Vehicle
UR - http://www.scopus.com/inward/record.url?scp=85182023112&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85182023112
T3 - CEUR Workshop Proceedings
BT - RCIS
PB - CEUR Workshop Proceedings
T2 - Workshop on Research Projects Track at 17th International Conference on Research Challenges in Information, RCIS 2023
Y2 - 23 May 2023 through 23 May 2023
ER -