Аннотация:
Keystroke dynamics recognition is a crucial element in enhancing security, enabling personalized user
authentication, and supporting various identity verification systems. This study offers a comparative
analysis of a nine-variate prediction ellipsoid for normalized data and machine learning algorithms
specifically, autoencoder, isolation forest, and one-class support vector machine for keystroke dynamics
recognition. Traditional methods often assume a multivariate normal distribution. However, real-world
keystroke data typically deviate from this assumption, negatively impacting model performance. To address
this, the dataset was normalized using the multivariate Box-Cox transformation, allowing the construction
of a decision rule based on a nine-variate prediction ellipsoid for normalized data. The study also includes
The results revealed that the application of the Box-Cox transformation significantly enhanced both the
accuracy and robustness of the prediction ellipsoid. Although all models demonstrated strong performance,
the nine-variate prediction ellipsoid for normalized data consistently outperformed the machine learning
algorithms. The study highlights the importance of careful feature selection and multivariate normalizing
transformations in keystroke dynamics recognition. Future studies could benefit from broader datasets that
include a wider range of user behaviors, such as variations in environmental factors and longer key
sequences.
