Application of a Nine-Variate Prediction Ellipsoid for Normalized Data and Machine Learning Algorithms for Keystroke Dynamics Recognition

Abstract

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.