MLP neural network for keystroke-based user identification system

Popovici, Eduard C.; Guta, Ovidiu G.; Stancu, Liviu A.; Arseni, Ștefan-Ciprian

doi:10.1109/telsks.2013.6704912

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Eduard et al [17] explore the use of multi-layer perceptrons (MLP) for keystroke based authentication. Their model considers time-based information between different keys separately, and does not aggregate information from the entire keystroke sequence.…”

Section: Related Workmentioning

confidence: 99%

“…However, all of these authentication methods suffer from some problems. For example, passwords can often be guessed and are sometimes stolen, and most biometric systems require special hardware [14,17,22]. Moreover, research has shown, for example, that the accuracy of face and fingerprint recognition on the elderly is lower than for young people [13].…”

Section: Introductionmentioning

confidence: 99%

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Free-Text Keystroke Dynamics for User Authentication

Li¹,

Chang²,

Stamp³

2021

Preprint

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In this research, we consider the problem of verifying user identity based on keystroke dynamics obtained from free-text. We employ a novel feature engineering method that generates image-like transition matrices. For this image-like feature, a convolution neural network (CNN) with cutout achieves the best results. A hybrid model consisting of a CNN and a recurrent neural network (RNN) is also shown to outperform previous research in this field.

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Section: Related Workmentioning

confidence: 99%