Research on Intrusion Detection Method Based on Generative Adversarial Network

Zhang, Junjie; Zhao, Ying

doi:10.1109/bdacs53596.2021.00065

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…After reparametrized sampling (which is equivalent to adding noise to the input) the latent variable z is obtained that obeys the prior probability distribution P(z). The aim is to address the neural network inverse gradient problem, ensuring that for each input sample x, there exists a corresponding latent variable z [23]. The solution of the latent variable is shown in Eq.…”

Section: Figurementioning

confidence: 99%

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A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

Wang,

Yan,

Jia

2024

CMC

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Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content. The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge. The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets, with the dataset's imbalance significantly affecting the model's performance. In the present study, a new model, referred to as UD-VLD (Unbalanced Dataset-VAE-LSTM-DRN), was proposed to address above problem. The proposed model is an encrypted traffic identification model for handling unbalanced datasets. The encoder of the variational autoencoder (VAE) is combined with the decoder and Long-short term Memory (LSTM) in UD-VLD model to realize the data enhancement processing of the original unbalanced datasets. The enhanced data is processed by transforming the deep residual network (DRN) to address neural network gradient-related issues. Subsequently, the data is classified and recognized. The UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique, thereby solving the processing problem for unbalanced datasets. The UD-VLD model was tested using the publicly available Tor dataset and VPN dataset. The UD-VLD model is evaluated against other comparative models in terms of accuracy, loss rate, precision, recall, F1-score, total time, and ROC curve. The results reveal that the UD-VLD model exhibits better performance in both binary and multi classification, being higher than other encrypted traffic recognition models that exist for unbalanced datasets. Furthermore, the evaluation performance indicates that the UD-VLD model effectively mitigates the impact of unbalanced data on traffic classification. and can serve as a novel solution for encrypted traffic identification.

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Section: Figurementioning

confidence: 99%

“…During the training process, both the recognition model and the generative model work in tandem to fine-tune neural network parameters. This collaborative effort allows them to progressively approximate the true posterior distribution [23].…”

Section: Figurementioning

confidence: 99%

A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

Wang,

Yan,

Jia

2024

CMC

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“…The original GAN is mainly based on full connectivity to implement the generative and discriminant networks [16]. GAN requires a large number of network parameters to generate images.…”

Section: Crdcgan Algorithmmentioning

confidence: 99%

GAN Data Augmentation Methods in Rock Classification

Zhao

Wang

et al. 2023

Applied Sciences

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In this paper, a data augmentation method Conditional Residual Deep Convolutional Generative Adversarial Network (CRDCGAN) based on Deep Convolutional Generative Adversarial Network (DCGAN) is proposed to address the problem that the accuracy of existing image classification techniques is too low when classifying small-scale rock images. Firstly, Wasserstein distance is introduced to change the loss function, which makes the training of the network more stable; secondly, conditional information is added, and the network has the ability to generate and discriminate image data with label information; finally, the residual module is added to improve the quality of generated images. The results demonstrate that by applying CRDCGAN to the augmented rock image dataset, the accuracy of the classification model trained on this dataset is as high as 96.38%, which is 13.39% higher than that of the classification model trained on the non-augmented dataset, and 8.56% and 6.27% higher than that of the traditional dataset augmented method and the DCGAN dataset augmentation method, respectively. CRDCGAN expands the rock image dataset, which makes the rock classification model accuracy effectively improved. The data augmentation method was found to be able to change the accuracy of the classification model to a greater extent.

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