Statistical Modeling of Keystroke Dynamics Samples For the Generation of Synthetic Datasets

Denis Migdal (1) , Christophe Rosenberger (1)

1 Equipe SAFE - Laboratoire GREYC - UMR6072

Biometrics is an emerging technology more and more present in our daily life. However, building biometric systems requires a large amount of data that may be difficult to collect. Collecting such sensitive data is also very time consuming and constrained, s.a. GDPR legislation in Europe. In the case of keystroke dynamics, most existing databases have less than 200 users. For these reasons, it is crucial for this biometric modality to be able to generate a significant and realistic synthetic dataset of keystroke dynamics samples. We propose in this paper an original approach for the generation of synthetic keystroke data given samples from known users as a first step towards the generation of synthetic datasets. Experimental results show the capability of the proposed statistical model to generate realistic samples from existing datasets in the literature.

Mots clés: Keystroke dynamics; Statistical modelling; Synthetic dataset; Data Analysis

Domaines: Cryptographie et sécurité [cs.CR]

Denis Migdal, Christophe Rosenberger. Statistical Modeling of Keystroke Dynamics Samples For the Generation of Synthetic Datasets. Future Generation Computer Systems, 2019, ⟨10.1016/j.future.2019.03.056⟩. ⟨hal-02087222⟩