Abstract: Machine learning-based authentication (MLBA) techniques may provide great advantages when combined with manual authentication methods. The contribution consists of detecting phenomena that are co-occurring with, or causally related to, both valid and invalid authentication attempts. Models may be built to detect those events by training them using labeled data. Acquiring labels is traditionally a difficult manual process that is human effort intensive. This invention solves that problem by leveraging multi-factor authentication as a tool to automate labeling.