Study on Construction of IOT Network Intrusion Detection Classification Model and Optimization Based on Combination of ResNet and Bidirectional LSTM Network

CHEN Hongsong, CHEN Jingjiu


 In order to improve the performance of the Internet of Things (IOT) network intrusion detection model, Residual Networks (ResNet) and bidirectional Long-Short Term Memory (LSTM) networks were combined,and an IOT intrusion detection classification model was constructed. For the rapid and batch processing problem of large-scale IOT traffic, multiple traffic samples were converted into grayscale images. Then,the grayscale images were used to construct IOT intrusion detection and classification model which combined with ResNet and bidirectional LSTM network. The network structure and re-usability of the classification model were optimized experimentally,so the optimization model was obtained finally. The classification accuracy of the optimization model is 96.77%, and the running time after the model reuse optimization is 39.85 s. Compared with other machine learning algorithms, the proposed approach achieves good results in both classification accuracy and efficiency. The performance of the proposed model is better than that of traditional intrusion detection model.



Keywords:  intrusion detection,  Residual Networks(ResNet),  bidirectional Long-Short Term Memory(LSTM) networks  image classification,  IOT(Internet of Things)

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