The rapid expansion of network systems has led to increased cyber threats, necessitating effective Intrusion Detection Systems (IDS). Traditional supervised models struggle to detect unknown attacks due to evolving attack patterns. To address this, training a Random Forest (RF) model with noise data enhances the identification of unseen attacks. Experimental results show improved accuracy and F1-score when RF is trained with noise data. Synthetic datasets demonstrate the effectiveness of RF in detecting unknown attacks. Benchmark IDS datasets also exhibit enhanced performance when RF is trained with noise data.
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by Md. Ashraf U... a las arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11180.pdfConsultas más profundas