In this work, we use the shallow learning and the deep learning methods to build a network threat detection model on the imbalanced data.
The experimental results show that our network threat detection model based on WGAN for oversampling achieves a good performance for network threat detection.
The experimental results show that our network threat detection model based on WGAN for oversampling achieves a good performance for network threat detection.
Sep 29, 2024 · Our findings reveal that SC-WGAN, surpasses existing methods in generating more representative samples and enhancing NIDS detection accuracy.
Missing: Threat | Show results with:Threat
Nov 5, 2021 · The experiment results show that CWGAN not only improve the training stability of WGAN on the loss smoother and closer to 0 but also improve the performance of ...
Class imbalance is a common problem in network threat detection. Oversampling the minority class is regarded as a popular countermeasure by generating ...
Sep 29, 2024 · Our findings reveal that SC-WGAN, surpasses existing methods in generating more representative samples and enhancing NIDS detection accuracy.
May 31, 2024 · We take the perspective of imbalance and high dimensionality of datasets in the field of intrusion detection and propose an oversampling technique based on ...
In this research, a novel approach for enhancing the performance of an NIDS through the integration of Generative Adversarial Networks (GANs) is proposed.
Aug 28, 2024 · This paper examines WGAN as a more advanced technique for addressing imbalanced data sets in the context of machine learning.
Missing: Threat | Show results with:Threat
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