MACHINE-LEARNING BEHAVIORAL ANALYSIS TO DETECT DEVICE THEFT AND UNAUTHORIZED DEVICE USAGE

The disclosure relates to machine-learning behavioral analysis to detect device theft and unauthorized device usage. In particular, during a training phase, an electronic device may generate a local user profile that represents observed user-specific behaviors according to a centroid sequence, wherein the local user profile may be classified into a baseline profile model that represents aggregate behaviors associated with various users over time. Accordingly, during an authentication phase, the electronic device may generate a current user profile model comprising a centroid sequence re-expressing user-specific behaviors observed over an authentication interval, wherein the current user profile model may be compared to plural baseline profile models to identify the baseline profile model closest to the current user profile model. As such, an operator change may be detected where the baseline profile model closest to the current user profile model differs from the baseline profile model in which the electronic device has membership.