To address the challenges of high-dimensional traffic feature redundancy and the scarcity of novel attack samples in 5G networks and IoT environments, this paper proposes a multimodal and collaborative intelligent detection framework named SMCE-ICHBO-CBGAR. This study focuses on three critical technical challenges. First, we propose a Synthetic Multi-Cluster Equilibrium (SMCE) Approach, which overcomes the limitations caused by distribution shifts in traditional balancing methods in adversarial attack scenarios by combining K-means feature space decoupling with a hybrid SMOTE enhancement technique. Secondly, we construct an Improved Chaotic Honey Badger Optimization (ICHBO) based on tent chaotic initialization and parallel fitness evaluation to accelerate feature selection. In addition, in order to solve the shortcomings of traditional intrusion detection methods in robustness and complex attack pattern recognition, this paper designs a spatiotemporal attention residual fusion model (CBGAR) through hybrid feature encoding of CNN and BiGRU, a multi-head self-attention mechanism, and a multi-scale residual connection. Experiments conducted on the CICIDS-2017 and CICIoT 2023 datasets show that the accuracy of the proposed model reaches 97.60% and 94.11% respectively, which significantly outperforms existing models in various evaluation indicators and effectively improves the intrusion detection performance.