The content discusses the challenges of detecting unknown attacks in IDS due to the lack of sufficient attack samples. It introduces a strategy of training supervised learning models with noise data labeled as attacks to improve detection accuracy. The study evaluates the performance of Random Forest models trained with and without noise data on synthetic and benchmark IDS datasets. Results show that incorporating noise data enhances the model's ability to identify previously unseen attacks.
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by Md. Ashraf U... a las arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11180.pdfConsultas más profundas