关键词: 物联网；入侵检测；类别感知；特征选择；加权集成学习

Abstract: To address high-dimensional feature redundancy, class imbalance, and limited detection performance in complex attack IoT (Internet of Things)scenarios, this study proposes an IoT Intrusion Detection method integrating Class-aware Feature selection and Weighted Ensemble learning(IID-CFWE). First, a Class-aware Adaptive recursive Feature Selection(CA-AFS) mechanism is developed by jointly considering global, class-aware, and boundary-sample feature importance, thereby reducing redundant features and enhancing the discrimination of minority attack classes. Second, a heterogeneous ensemble learning model combining random-search optimization with weighted soft voting is constructed. Random search is employed to optimize the hyperparameters of base classifiers, while the class probability outputs of base classifiers are weighted according to their validation performance and fused by weighted soft voting, enabling the model to exploit their complementary discriminative capabilities. The class-probability outputs of the base classifiers are then aggregated through weighted soft voting to improve detection performance and classification stability. Extensive experiments are conducted on the public N-BaIoT and CIC-IDS-2017 datasets. The results indicate that the proposed method effectively improves intrusion detection performance while reducing feature redundancy, providing an efficient solution for IoT network security.

Key words: internet of things (IoT); intrusion detection; class-aware; feature selection; weighted ensemble learning