We propose a deep adversarial insider threat detection (DAITD) framework using the Generative Adversarial Networks (GAN) to approximate the true anomalous ...
We propose a deep adversarial insider threat detection (DAITD) framework using the Generative Adversarial Networks (GAN) to approximate the true anomalous ...
This work proposes a deep adversarial insider threat detection (DAITD) framework using the Generative Adversarial Networks (GAN) to approximate the true ...
The proposed framework has the ability to address the challenge of predicting insider threat instances as well as the approximate time of occurrence. This study ...
To enrich the diversity of the synthetic samples, we propose a deep adversarial insider threat detection (DAITD) framework using the Generative Adversarial ...
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Sep 1, 2024 · We propose SPCAGAN, a linear manifold learning-based regularization on ACGAN, to generate insider data samples to reduce the effects of class ...
Apr 12, 2022 · This thesis explores the application of Generative Adversarial Networks (GANs) in augmenting insider threat detection datasets to alleviate class imbalance.
One of the ways to deal with this issue is called data augmentation. In this paper, we apply data augmentation in the design of cyber-attack detection methods ...
Mar 6, 2022 · We propose a linear manifold learning-based generative adversarial network, SPCAGAN, that takes input from heterogeneous data sources and adds a ...
Researchers have proposed a method that utilizes data augmentation to generate synthetic data for training deep learning models in insider threat detection.