Reliable and Trustworthy Local–Global Hierarchical Framework for Intrusion Detection in 6G-IoT Networks

6G-driven Internet of Things (IoT) networks demonstrate enormous development potential. However, complex and diverse IoT devices also pose severe cybersecurity challenges. To address the problem of low reliability and trustworthiness of existing intrusion detection systems (IDS) in large-scale, low-latency scenarios of 6G-IoT networks, this paper proposes a local-global hierarchical Framework for intrusion detection. First, a reliable feature processing algorithm is designed based on fusion features and a multi-objective collaborative optimization mechanism oriented toward normal behavior modeling, enabling the binary classification model to effectively detect unknown types of attack traffic even under data anomaly conditions. Second, an adaptive classification trustworthiness assessment method based on classification information entropy and collaborative verification is proposed, which can accurately locate and upload suspicious traffic during the classification process. Finally, we propose a gradient-oriented feature decoupling module that effectively improves the feature learning capacity of network attack detection models in global servers for minority class samples. Experimental results demonstrate that our proposed method achieves optimal performance on five processed IDS benchmark datasets, with local IoT devices achieving an F1 score of 98.5% and global servers achieving an arithmetic mean F1 score of 96.6%, providing strong support for the reliable operation of IDS in 6G-IoT networks.