IoT Network Security Threat Detection Algorithm Integrating Symmetric Routing and a Sparse Mixture-of-Experts Model
Jiawen Yang, Kunsan Zhang, Renguang Zheng, Chaopeng Li, Jiachun Zheng · Symmetry · 2025
With the rapid deployment of the Internet of Things (IoT) in critical domains such as power and industrial systems, the number of IoT devices has surged, accompanied by increasingly severe network security risks. IoT networks face diverse threats, including distributed denial-of-service attacks, advanced persistent threats, and data theft or tampering, while traditional detection and defense, lacking deep feature analysis, struggle with complex and unknown attacks, degrading security threat event detection. To this end, this paper proposes an IoT network security threat detection algorithm that integrates symmetric linear routing with a sparse mixture-of-experts model. The algorithm consists of a ConvNeXt feature extractor and a sparse BiLSTM expert layer, with symmetric linear routing embedded in the gating module. ConvNeXt provides refined global and local representations, Top-K gated BiLSTM experts for the module sequence-level dependencies among ordered features, and symmetric linear routing suppresses routing bias, enabling efficient and robust detection of IoT security threats. Experimental results on the CIC-IDS2018, TON-IoT, and BoT-IoT datasets indicate that the proposed IoT network security threat detection algorithm achieves accuracies of 94.08%, 99.99±0.01%, and 99.78%, respectively. Comparative experiments show the proposed algorithm outperforms baseline and state-of-the-art models, while the ablation and Top-K studies confirm module effectiveness for IoT intrusion detection.