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... alle arxiv.org 03-19-2024
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