Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts Article Swipe

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YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2509.24807

Keystroke dynamics is a promising modality for active user authentication, but its effectiveness under varying LLM-assisted typing and cognitive conditions remains understudied. Using data from 50 users and cognitive labels from Bloom's Taxonomy, we evaluate keystroke-based authentication in Korean across three realistic typing scenarios: bona fide composition, LLM content paraphrasing, and transcription. Our pipeline incorporates continuity-aware segmentation, feature extraction, and classification via SVM, MLP, and XGB. Results show that the system maintains reliable performance across varying LLM usages and cognitive contexts, with Equal Error Rates ranging from 5.1% to 10.4%. These findings demonstrate the feasibility of behavioral authentication under modern writing conditions and offer insights into designing more context-resilient models.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2509.24807
PDF: https://arxiv.org/pdf/2509.24807
OA Status: green
OpenAlex ID: https://openalex.org/W4415337500

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OpenAlex ID: https://openalex.org/W4415337500

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DOI: https://doi.org/10.48550/arxiv.2509.24807

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Title: Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-09-29

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Authors: Dong Hyun Roh, Rajesh Kumar Kaushal

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Landing page: https://arxiv.org/abs/2509.24807

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PDF URL: https://arxiv.org/pdf/2509.24807

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2509.24807

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Cited by: 0

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