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- W4386804922 abstract "The base of a typing-based identification system (TBIS) is uniqueness in keystroke-based typing manner of a person. Through deep training and learning process, a machine can be empowered to respond after minutely observing some events or changes in a better way than a human. On account of this, any sensitive digital machine can acquire one level up security when deep learning joins its hands with keystroke dynamics. Numerous deep TBISs have been cited, though the methodical investigation is still rarely available on recent studies on machine learning. Prevailing surveys, reviews and comparative analysis-based research works are considered here in this paper to cover keystroke typing manner with deep neural models. A perceptive description of latest contributions to grow a TBIS with deep-knowledge approaches is provided here broadly. In addition, almost all the already utilized standard datasets (Benchmark datasets) and their feature illustration methods (on the basis of number-counting, picture and time-series) are likewise reviewed in this paper. This paper finally sums up by providing a relative scrutiny of all TBISs developed by using deep learning neural network models. It eventually enlightens the future researchers to invent and apply their ideas with novelty to fill the gap." @default.
- W4386804922 created "2023-09-17" @default.
- W4386804922 creator A5034554798 @default.
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- W4386804922 date "2023-01-01" @default.
- W4386804922 modified "2023-09-27" @default.
- W4386804922 title "Review of Deep-Learning Based User-Identification by Typing Behavior" @default.
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- W4386804922 doi "https://doi.org/10.1007/978-981-99-3716-5_6" @default.
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