Research on network intrusion detection methods based on deep learning

Jionghao Li; Hui Zhao

doi:10.1117/12.3051760

18 November 2024 Research on network intrusion detection methods based on deep learning

Jionghao Li, Hui Zhao

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134032W (2024) https://doi.org/10.1117/12.3051760
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

In network intrusion datasets, the normal traffic is usually magnitude hundreds or even thousands of times more than the minority class attacks, and this dataset imbalance creates greater difficulties in deep learning model training, often leading to the model not being able to effectively learn the data features of the minority class, which in turn reduces the detection efficiency. Therefore, an effective solution is proposed to balance the normal traffic by utilizing WGAN for minority class data augmentation and random undersampling technique for random censoring, aiming to improve the overall model and the detection rate of minority class attacks. Finally, an intrusion detection model based on hybrid neural network for feature extraction is designed to address the shortcomings of existing intrusion detection models in terms of detection capability and structure. In the convolutional part, three different scales of convolutional kernels are used to perform feature extraction on the input data in order to obtain richer feature representations. Meanwhile, a bidirectional long short-term memory (BiLSTM) network is utilized to learn the temporal relationship between features, and a Transformer encoder is introduced to establish the connection between different features.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jionghao Li and Hui Zhao "Research on network intrusion detection methods based on deep learning", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134032W (18 November 2024); https://doi.org/10.1117/12.3051760

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KEYWORDS

Computer intrusion detection

Deep learning

Detection and tracking algorithms

Feature extraction

Convolution

Data modeling

Machine learning

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