Abstract
With the increasingly complex heterogeneity of device types, communication protocols and data formats in the Internet of Things (IoT) environment, traditional intrusion detection (ID) models are difficult to effectively deal with dynamic threats. This paper proposes an intelligent IoT intrusion identification model HMG-AID for heterogeneous environments. The model realizes end-to-end ID by fusing multi-modal feature extraction, graph attention mechanism and dynamic trust evaluation. It uses one-dimensional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to extract spatiotemporal characteristics of traffic data in parallel, constructs heterogeneous device graphs and aggregates neighbor information through GraphSAGE, then introduces multi-head graph attention-weighted key nodes, and finally integrates zero-trust dynamic evaluation to optimize classification decisions. The F1 scores of the HMG-AID model on the three datasets of NSL-KDD, CICIDS2017, and ToN-IoT are 94.7%, 95.3%, and 94.9%, respectively, which are significantly better than the baseline model. Moreover, the F1 score retention rate under-5 dB noise interference is 85.6%, and the F1 score retention rate under counterattack attack is 88.9%. In addition to this, the model has a 93.6% F1 score retention rate when the device size increases to 10,000 nodes. The model effectively improves detection accuracy, environmental adaptability and decision interpretability through multi-level innovative design and provides a reliable theoretical basis and practical framework for heterogeneous IoT security protection.
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