IoT intrusion detection technology based on Deep learning
Bo Cao, Chenghai Li, Junfeng Sun, Yafei Song · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · 2022
To address the problem of low accuracy of existing network intrusion detection models for multi-classification of intrusion behaviors and redundancy of data features, a network intrusion detection model incorporating convolutional neural networks and gated recurrent units is proposed. To solve the problem of feature redundancy, feature selection is performed by combining random forest algorithm and Pearson correlation analysis; after that, temporal features of data are extracted by TCN and GRU, while attention module is introduced to assign different weights to features, thus reducing overhead and improving model performance; finally, Softmax function is used for classification. In this paper, the proposed model is evaluated on the Bot-Lot dataset with an accuracy of 99.99%.