A comparative study of secret code variants in terms of keystroke dynamics

A Survey of Keystroke Dynamics Biometrics

“…Database partitioning [ 20 ] and retraining during system idle period [ 55 ] has been suggested as an attempt to resolve this problem. Some widely used neural networks are radial basis function network [ 9 , 49 ], learning vector quantization [ 62 , 129 ], multilayer perceptron [ 24 , 80 , 86 ], and self-organizing map [ 130 , 131 ].…”

“…Even though several research works has claimed to involve population from broad age distribution (20 to 60) [ 55 , 66 , 79 ], emphasis should be placed on a more important aspect, such as the typing proficiency of these users. Apart from [ 12 ], where the whole population consists of skilled typists, others involved untrained typists who are familiar with the input device [ 80 , 81 ]. However, none of the experiments specifically conducted on users that come from entirely low typing proficiency.…”

Contributions to empirical analysis of keystroke dynamics in passwords

“…This heuristics is in agreement with empirical conclusions presented by Giot et al [9], according to which "it is better to choose passwords based on short, simple and known words". Indeed, habituation/familiarity of the keyed string was also considered as a relevant factor in other works, such as [6,17,18,22].…”

Developing of keystroke dynamics analyzing systems using a presentation based on state contexts

“…In addition, adding, deleting, or updating a biometric image in a database will generally lead to retraining of neural networks. To solve the problem of user identification by keystroke dynamics, various types of neural networks are used: direct propagation (Pavaday et al, 2007), multilayer neural networks (Pavaday & Soyjaudah, 2008), recurrent networks (Kobojek & Saeed, 2016) and self-organizing maps (Loy et al, 2007).…”