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KeyRecs: A keystroke dynamics and typing pattern recognition dataset.

ABSTRACT: Keystroke dynamics can valuably contribute to the development of intelligent authentication systems by enabling a single and continuous authentication process in a passive and non-intrusive manner by continuously verifying a user's identity. This work describes the KeyRecs dataset, which contains fixed-text and free-text samples of user typing behavior and demographic information of the participants age, gender, handedness, and nationality. The keystroke data was obtained from 99 participants of various nationalities who completed password retype and transcription exercises. The recorded samples consist of inter-key latencies computed in a digraph fashion measuring the time between each key press and release during an exercise. KeyRecs can be leveraged to improve the recognition of authorized users and prevent unauthorized access in biometric authentication software.

SUBMITTER: Dias T

PROVIDER: S-EPMC10474054 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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KeyRecs: A keystroke dynamics and typing pattern recognition dataset.

Dias Tiago T Vitorino João J Maia Eva E Sousa Orlando O Praça Isabel I

Data in brief 20230821

Keystroke dynamics can valuably contribute to the development of intelligent authentication systems by enabling a single and continuous authentication process in a passive and non-intrusive manner by continuously verifying a user's identity. This work describes the KeyRecs dataset, which contains fixed-text and free-text samples of user typing behavior and demographic information of the participants age, gender, handedness, and nationality. The keystroke data was obtained from 99 participants of ...[more]

PMID: 37663780

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