Enhancing 6G-IoT Network Security: A Trustworthy and Responsible AI-Driven Stacked-Hybrid Model for Attack Detection

Abstract

The fast growth of 6G-enabled Internet of Things (IoT) networks has transformed communication and made it possible for smart cities, driverless cars, healthcare, and industrial automation to all have seamless connectivity. However, the growing complexity and diversity of 6G-IoT infrastructures present serious cybersecurity issues, leaving these networks open to a range of attacks like malware dissemination, spoofing, and Distributed Denial-of-Service (DDoS). Because traditional intrusion detection systems (IDS) cannot adjust to changing attack patterns, they are unable to effectively combat these threats. Using Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost) to improve detection accuracy and robustness, this study proposed a novel Stacked-Hybrid Model for attack detection in 6G-IoT networks. The proposed model maximizes classification performance while reducing computational cost by utilizing feature selection and ensemble learning. Training and evaluation are conducted on the RT-IoT dataset, proving the effectiveness of the method. The experimental results demonstrate that the proposed Stacked-Hybrid Model outperforms individual machine learning (ML) models, achieving 99.90% detection accuracy. The other performance metrics of the proposed model have also been evaluated including precision, sensitivity, specificity and F1-score rates at 99.56%, 99.56%, 99.93% and 99.56% respectively. Significant advancements in identifying intricate and dynamic cyber threats in 6G-IoT contexts are also revealed by a comparison with other models.

Topics & Concepts