Feature randomization for securing machine learning models includes receiving an event, and altering, responsive to receiving the event, a threshold pseudo-randomly to generate an altered threshold value. Feature randomization further includes applying the altered threshold value to a threshold-dependent feature to generate an altered threshold-dependent feature value. The altered threshold-dependent feature value determined at least in part from the event. Feature randomization further includes executing a machine learning model, on the event and the altered threshold-dependent feature value, to generate a predicted event type for the event.