Conventional authentication mechanisms, such as passwords, are becoming less effective due to technological progress and evolving attack methodologies. To address this vulnerability, continuous authentication has emerged as a viable approach, wherein a user's identity is perpetually monitored after initial login to ensure an unauthorized individual has not hijacked the session. Mouse dynamics, which quantifies the behavioral patterns of a user's cursor movements, represents a highly promising biometric indicator for these ongoing verification models. This paper extends our prior research by benchmarking a 40-user dataset across three machine learning and three deep learning models. Our investigation covers two distinct evaluation frameworks: binary classification for individual authentication—where a 1-dimensional convolutional neural network (1D-CNN) yielded the highest performance, achieving a peak average test accuracy of 85.73% for the top-10 users—and multi-class classification utilizing an artificial neural network (ANN), which achieved an unprecedented peak accuracy of 92.48%, establishing a new performance benchmark for this specific dataset.
Transparent Intelligence Publishing