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This paper compares XGBoost and RoBERTa-LoRA for network intrusion detection across same-dataset, cross-dataset transfer, and adversarial evasion axes, showing that no universal winner exists and performance depends on the evaluation condition.
This arXiv paper proposes a three-class detection framework distinguishing humans, bots, and AI agents, showing binary classifiers systematically misclassify agents. It identifies minimal feature sets (e.g., mouse_event_rate and teleport_click_ratio) that achieve perfect agent recall across evasion levels.