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.

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

Unmanned Aerial Vehicle,Deep neural networks,Calibration,Uncertainty,Reliability,Jamming identification,5G,6G

• Computer and Information Sciences - Natural Sciences