融合残差与注意力机制，实现工业物联网实时入侵检测
Hybrid ResNet-1D-BiGRU with Multi-Head Attention for Cyberattack Detection in Industrial IoT Environments
- 用1D残差网络+双向GRU+多头注意力提取时空特征
- 在两个数据集上准确率超98%，推理延迟低于0.0002秒/实例
- 适合需要低延迟、高精度的工业物联网安全场景
本研究提出一种混合深度学习模型，用于工业物联网（IIoT）系统中的入侵检测，结合ResNet-1D、BiGRU与多头注意力（MHA）实现有效的时空特征提取和注意力加权。为缓解类别不平衡问题，在EdgeHoTset数据集上训练时采用SMOTE。模型在该数据集上达到98.71%准确率，损失0.0417%，推理延迟仅0.0001秒/实例，表现出优异的实时性能。为评估泛化能力，模型还在CICIoV2024数据集上测试，取得99.99%准确率和F1-score，损失0.0028，0%假阳性率，推理时间0.00014秒/实例。在所有指标与数据集上，该模型均优于现有方法，验证了其在实时物联网入侵检测中的鲁棒性与有效性。
原文摘要 · Abstract (English)
This study introduces a hybrid deep learning model for intrusion detection in Industrial IoT (IIoT) systems, combining ResNet-1D, BiGRU, and Multi-Head Attention (MHA) for effective spatial-temporal feature extraction and attention-based feature weighting. To address class imbalance, SMOTE was applied during training on the EdgeHoTset dataset. The model achieved 98.71% accuracy, a loss of 0.0417%, and low inference latency (0.0001 sec /instance), demonstrating strong real-time capability. To assess generalizability, the model was also tested on the CICIoV2024 dataset, where it reached 99.99% accuracy and F1-score, with a loss of 0.0028, 0 % FPR, and 0.00014 sec/instance inference time. Across all metrics and datasets, the proposed model outperformed existing methods, confirming its robustness and effectiveness for real-time IoT intrusion detection.
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