Why did my detector do that?!: predicting keystroke-dynamics error rates

Abstract

Abstract. A major challenge in anomaly-detection studies lies in identifying the myriad factors that influence error rates. In keystroke dynamics, where detectors distinguish the typing rhythms of genuine users and impostors, influential factors may include the algorithm itself, amount of training, choice of features, use of updating, impostor practice, and typist-to-typist variation. In this work, we consider two problems. (1) Which of these factors influence keystroke-dynamics error rates and how? (2) What methodology should we use to establish the effects of multiple factors on detector error rates? Our approach is simple: experimentation using a benchmark data set, statistical analysis using linear mixed-effects models, and validation of the model’s predictions using new data. The algorithm, amount of training, and use of updating were strongly influential while, contrary to intuition, impostor practice and feature set had minor effect. Some typists were substantially easier to distinguish than others. The validation was successful, giving unprecedented confidence in these results, and establishing the methodology as a powerful tool for future anomaly-detection studies.