Edge network intrusion detection method based on feature selection and TextCNN

Hongkai Wang; Sichen Pan; Xiaoming Ju; Yongming Feng

doi:10.1117/12.2682500

26 May 2023 Edge network intrusion detection method based on feature selection and TextCNN

Hongkai Wang, Sichen Pan, Xiaoming Ju, Yongming Feng

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127003E (2023) https://doi.org/10.1117/12.2682500
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

In recent years, the concept of edge computing has risen. The network computing structure has gradually changed from a centralized cloud service cluster to a cloud-edge collaborative service system. But at the same time, the limited resources of edge nodes and the complex network environment make it easier to become the target of network intrusion, resulting in the collapse of nodes. Aiming at the security threats faced by edge nodes, we propose an edge network intrusion detection method based on feature selection and TextCNN. Enhance the correlation between features and eigenvalues through feature selection, and reduce the intrusion detection cost of nodes. The feature information of the sample is extracted through TextCNN, and the feature information is combined and correlated through the proposed correlation pooling layer, so as to improve the accuracy of model detection. Finally, the method is tested on the kddcup99 dataset. Compared with traditional methods, the accuracy, recall and f1 score of the model are improved, which proves the feasibility and effectiveness of the method.

Citation Download Citation

Hongkai Wang, Sichen Pan, Xiaoming Ju, and Yongming Feng "Edge network intrusion detection method based on feature selection and TextCNN", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127003E (26 May 2023); https://doi.org/10.1117/12.2682500

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KEYWORDS

Computer intrusion detection

Feature selection

Feature extraction

Machine learning

Convolution

Data modeling

Education and training

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