Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection

Xu, Yanwu; Zhang, Xiaoyu; Qiu, Zhenliang; Zhang, Xia; Qiu, Jian; Zhang, Hua

doi:10.1155/2021/9206440

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Lee and Park [22] proposed a GANbased fusion detection system for imbalanced data. Xu et al [23] proposed a convergent Wasserstein GAN to solve the problem of class imbalance in network threat detection. Huang and Lei [24] proposed a novel Imbalanced GAN (IGAN) to deal with the problem of the class imbalance.…”

Section: Sample Imbalancementioning

confidence: 99%

“…WGAN uses the Wasserstein distance, which has superior smoothing properties compared to Jense-Shannon (JS) and solves the gradient disappearance problem [23]. In addition, WGAN addresses not only the problem of GAN training instability but also provides a reliable indicator of the training process, and the indicator is highly correlated with the quality of the generated samples.…”

Section: Algorithmmentioning

confidence: 99%

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Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

Shi

Ren

et al. 2022

Journal of Sensors

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The traditional Infrastructure as a Service (IaaS) cloud platform tends to realize high data availability by introducing dedicated storage devices. However, this heterogeneous architecture has high maintenance cost and might reduce the performance of virtual machines. In homogeneous IaaS cloud platform, servers in the platform would uniformly provide computing resources and storage resources, which effectively solve the above problems, although corresponding mechanisms need to be introduced to improve data availability. Efficient storage resource availability management is one of the key methods to improve data availability. As mechanical hard disk is the main way to realize data storage in IaaS cloud platform at present, timely and accurate prediction of mechanical hard disk failure and active data backup and migration before mechanical hard disk failure would effectively improve the data availability of IaaS cloud platform. In this paper, we propose an improved algorithm for early warning of mechanical hard disk failures. We first use the Relief feature selection algorithm to perform parameter selection. Then, we use the zero-sum game idea of Generative Adversarial Networks (GAN) to generate fewer category samples to achieve a balance of sample data. Finally, an improved Long Short-Term Memory (LSTM) model called Convolution-LSTM (C-LSTM) is used to complete accurate detection of hard disk failures and achieve fault warning. We evaluate several models using precision, recall, and Area Under Curve (AUC) value, and extensive experiments show that our proposed algorithm outperforms other algorithms for mechanical hard disk warning.

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Section: Sample Imbalancementioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

Shi

Ren

et al. 2022

Journal of Sensors

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“…Li et al used WGAN-GP network to generate rice disease image samples, expanded the small sample set of rice disease image, and effectively enhanced the model training and learning effect [8]. Xu et al [9] proposed an oversampling model based on convergent WGAN, called convergent WGAN (CWGAN), in order to improve the training stability of GAN oversampling to detect network threats. The training process of CWGAN consists of multiple iterations.…”

Section: Introductionmentioning

confidence: 99%

Tunnel Lining Defect Identification Method Based on Small Sample Learning

Жу

Chen

et al. 2022

Wireless Communications and Mobile Computing

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Aiming at the problem of insufficient number of samples due to the difficulty of data acquisition in the identification of tunnel lining defects, a generative adversarial network was introduced to expand the data, and the network was improved for the mode collapse problem of the traditional generative adversarial network and the problem that the generated image features were not obvious. On the basis of the WGAN-GP network, a deep convolutional network is selected as its backbone network, and the effectiveness of the deep convolutional network in feature extraction by Lv et al. (2022) is used to improve the quality of the images generated by the network. In addition, the residual module is introduced into the discriminator network, and the upsampling module is introduced into the generator network, which further solves the problem of gradient disappearance of the two networks during the update iteration process through the idea of cross-connection, while better retaining the underlying features, which effectively solves the problem of mode collapse and low quality of generated images in the generative adversarial network. Compared with the original network, the image quality of the generated adversarial network is improved, and the discriminator and generator losses converge faster. At the same time, the recognition accuracy of the YOLOv5 network is improved by 4.4% and the overfitting phenomenon is alleviated, which proves the effectiveness of the method under the limited training data set.

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Adversarial Defense Mechanisms for Detecting and Mitigating Cyber-Attacks in Wireless Sensor Networks

Umeshbhai,

Kanubhai,

Bilal

et al. 2024

Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs)

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This chapter introduces ML-Defend, a novel defense mechanism tailored for detecting and mitigating cyber-attacks in wireless sensor networks (WSNs). By combining support vector machines (SVMs) for anomaly detection and convolutional neural networks (CNNs) for classification, ML-Defend harnesses the complementary strengths of these algorithms. This hybrid approach significantly enhances the system's ability to detect and mitigate cyber threats, thereby bolstering the security of WSNs. Through simulations and experiments, the authors demonstrate the efficacy of ML-Defend in accurately identifying and neutralizing attacks while minimizing false positives. This amalgamation of SVMs and CNNs presents a promising avenue for bolstering the cyber-defense capabilities of wireless sensor networks, ensuring their resilience against evolving threats in dynamic environments.

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Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection

Cited by 3 publications

References 21 publications

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

Tunnel Lining Defect Identification Method Based on Small Sample Learning

Adversarial Defense Mechanisms for Detecting and Mitigating Cyber-Attacks in Wireless Sensor Networks

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