A predictive contrivance for recognising traits in keystroke dynamics

Abstract

Predicting personal traits, particularly age group, gender, handedness, and hand(s) used, in the form of digital identity for smartphone users by analysing keystroke dynamics (KD) attributes is a challenging area. However, it has a variety of applications in e-commerce, e-banking, e-teaching/learning, e-exams, forensics, and social networking. The main bottleneck of this problem is addressing the imbalanced nature of KD datasets using conventional machine learning (ML) approaches. By their inherent nature, KD datasets are often imbalanced from various perspectives due to the non-uniformity of diverse user traits and their varied usage patterns. This study proposes a predictive model for both fixed and free-text modes, considering the effect of attached smartphone sensors. We adopt a score-level fusion of eXtreme Gradient Boosting (XGBoost) models on several balanced bootstrapped training samples to address the limitations of conventional approaches. This ensemble approach utilizes multiple bootstrapped training sets, where the class distribution in each set is equally balanced for more accurate and robust performance. Furthermore, we observe the positive impact of incorporating these prediction scores and labels with primary biometric attributes in KD-based user authentication and identification, both in static/entry-point and continuous/active security designs—a previously unanswered challenges. The predictive mechanism and its adaptation in unique KD-based designs, based on datasets collected from a considerable number of volunteers with diverse age groups, genders, professions, and education levels through a smartphone in a web environment, demonstrate the novelty of our approach.