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Articles

A Hybrid Multi-Model Framework for Personalized User-Level Anomaly Detection With Data-Driven Threshold Optimization

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By Amit Kumar, Om Pal, Wakar Ahmad
| preprints.org
You are already at the latest version Modern user authentication systems increasingly need user and device behavior aware adaptive mechanism to detect evolving threats beyond the traditional authentication framework of static credential verification. This paper proposes a hybrid multi-model framework for personalized user-level anomaly detection using a data-driven Hybrid Anomaly Score (HAS).
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