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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： He, Jiaxing Wang, Xiaodan Xiang, Qian Air Force Engn Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9783031189067;9783031189074

Radar high resolution range profile (HRRP) contains important structural features such as target size and scattering center distribution, which has attracted extensive attention in the field of radar target recognition. Aiming at the problems of class imbalance and insufficient data quality generated by vanilla GAN, an HRRP target recognition method based on improved conditional Wasserstein Variational Autoencoder Generative Adversarial Networks (CWVAEGAN) and one-dimensional convolutional neural network (CWVAEGAN-1DCNN) is proposed. CWVAEGAN class-balancing method introduces the concept of variational lower bound in VAE into GAN, and uses the encoded hidden vector instead of Gaussian noise as generator's input, which can significantly improve the fidelity of the generated samples and balance the class distribution of the dataset. The balanced dataset is used for classification through a standard 1D-CNN. The experimental results show that the samples generated by CWVAEGAN class-balancing method greatly improve the detection accuracy of 1D-CNN for minority classes, the fidelity of the samples generated by CWVAEGAN is higher than that of other class-balancing methods, and CWVAEGAN-1DCNN has strong HRRP target recognition ability.

关键词： High resolution range profile class-balancing method Generative adversarial network Variational autoencoder One-dimensional convolution neural network

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Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks

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APPLIED INTELLIGENCE 2023年第10期53卷 12416-12436页

作者： He, Jiaxing Wang, Xiaodan Song, Yafei Xiang, Qian Chen, Chen Air Force Engn Univ Changle East Rd Xian Shaanxi Peoples R China

There is a class-imbalance problem that the number of minority class samples is significantly lower than that of majority class samples in common network traffic datasets. class-imbalance phenomenon will affect the performance of the classifier and reduce the robustness of the classifier to detect unknown anomaly detection. And the distribution of the continuous features in the dataset does not follow the Gaussian distribution, which will bring great difficulties to intrusion detection. We propose Conditional Wasserstein Variational Autoencoders with Generative Adversarial Network (CWVAEGAN) to solve the class-imbalance phenomenon, CWVAEGAN transform the original dataset through data preprocessing, and then use the improved VAEGAN to generate minority class samples. According to the CWVAEGAN model, an intrusion detection system based on CWVAEGAN and One-dimensional convolutional neural networks (1DCNN), namely CWVAEGAN-1DCNN, is established. By using the examples generated by CWVAEGAN, the problem of intrusion detection on class unbalanced data is solved. Specifically, CWVAEGAN-1DCNN consists of three modules: data preprocessing module, CWVAEGAN, and deep neural network. We evaluate the performance of CWVAEGAN-1DCNN on two benchmark datasets and compared it with the other 16 methods. Experiment results suggest that the performance of CWVAEGAN-1DCNN is better than class-balancing methods, and other advanced methods.

关键词： Intrusion detection system (IDS) class-balancing method Generative adversarial network Variational autoencoder Gaussian mixture model

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