Innovative trust and privacy solutions for IOT devices in 5G environments
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32749Keywords:
5G-IOT security, IDS Technology, cyberattack detection, Machine learning ensemble.Abstract
This research study presents an ensemble-based intrusion detection framework for fifth-generation (5G)-driven Internet of Things (IoT) devices. It combines XGBoost, Random Forest, SVM, KNN and Logistic Regression with weighted soft voting in the model as well. The framework has been validated against the CSE-CICIDS2018 dataset, which encompasses different, state-of-the-art attack patterns associated with 5G-IoT systems. Normalisation, feature selection, and missing value imputation are performed in a preprocessing pipeline to improve generalisation. K-fold cross-validation ensures the robustness of the model; statistical testing confirms improvements in performance. The ensemble obtains an accuracy of 99.15%, an F1-score of 99.14% and an AUC of 1.00, outperforming each classifier alone. Privacy awareness is through flow-based features without inspecting Raw payloads. However, future work remains to be done on real-world validation and more intentional trust mechanisms.
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Copyright (c) 2026 Ahmed Naeem Jasim, Hussein M. Jebur, Mohammed Yousif Arabi
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