The study addresses the critical issue of backdoor attacks in URL detection using ensemble trees. It emphasizes the importance of defense mechanisms against malicious URL manipulation. The proposed innovative alarm system successfully detects poisoned labels and improves model robustness. Experimental results show the effectiveness of the defense method in mitigating Label Flipping attacks.
The research delves into the impact of random LF attacks on RF classifiers, showcasing successful manipulation and detection scenarios. The defense strategy based on K-NN approach proves effective in recovering poisoned labels and enhancing model accuracy. The study contributes valuable insights into ML security and countermeasures against adversarial threats.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Ehsan Nowroo... at arxiv.org 03-06-2024
https://arxiv.org/pdf/2403.02995.pdfDeeper Inquiries