CycleGAN-based intrusion detection data augmentation model

Yaping Jiang; Zhenghe Zhang; Yangtao Ge

doi:10.1117/12.3031413

19 July 2024 CycleGAN-based intrusion detection data augmentation model

Yaping Jiang, Zhenghe Zhang, Yangtao Ge

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318152 (2024) https://doi.org/10.1117/12.3031413
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

A model is proposed to address the problem of imbalanced sample data classes in response to attack behavior in intrusion detection systems based on deep learning technology, using an optimized CycleGAN (Cyclic Consistency Adversarial Network) to expand the sample of intrusion detection data. Firstly, the original dataset is divided into data categories and the adversarial generation network GAN is trained. Then, based on CycleGAN, a double-layer adversarial generation network is used to establish dual constraints on it to obtain new sample data, which can alleviate the impact of data class imbalance on classification tasks and enhance the key feature representation of the generated samples. The experimental results conducted on the KDDCUP99 dataset showed that using raw data and enhanced sample data to train the DNN detection classifier, the average recognition rate for U2R and R2L was significantly improved after using the enhanced dataset, and the overall average recognition accuracy was also slightly improved.

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

Citation Download Citation

Yaping Jiang, Zhenghe Zhang, and Yangtao Ge "CycleGAN-based intrusion detection data augmentation model", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318152 (19 July 2024); https://doi.org/10.1117/12.3031413

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KEYWORDS

Education and training

Data modeling

Computer intrusion detection

Statistical modeling

Deep learning

Deep convolutional neural networks

Statistical analysis

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