Computer user authentication using machine learning

A method comprising:

• initiating logon of a user;
• determining an identification confidence score for the user as part of a pre-authentication procedure which includes a user conduct evaluation in which circumstances surrounding the logon by the user are evaluated, the identification confidence score indicating a level of trust that the user is who it purports to be;
• initiating authentication for the user based on the identification confidence score of the user being below a predefined level, the authentication comprising:
• continuously monitoring, using a trained machine learning model unique to the user, both of user conduct and behavioral biometrics associated with the user in connection with the utilization of one or more resources by the user to generate first data;
• determining, based on the monitoring, differences between the first data and historical utilization data for the user to determine whether the user's utilization of the one or more resources is anomalous;
• and
• removing, when the user's utilization of the one or more resource is anomalous, the user's access to the one or more resources;
• and
• bypassing the authentication for the user based on the identification confidence score of the user being at or above the predefined level.

A method comprising:

• initiating logon of a user;
• determining an identification confidence score for the user as part of a pre-authentication procedure which includes (i) a user conduct evaluation in which circumstances surrounding the logon by the user are evaluated including two or more of web browser configuration, language, installed fonts, browser plug-ins, device IP address, screen resolution, browser cookie settings, geographical location, time zone, and geo-velocity and (ii) a device registration evaluation in which a user device is recognized as being associated with the user based either on a device identifier or a public key certificate, the identification confidence score indicating a level of trust that the user is who it purports to be;
• initiating authentication for the user based on the identification confidence score of the user being below a predefined level;
• continuously monitoring, using a trained machine learning model unique to the user, both of user conduct and behavioral biometrics associated with the user in connection with the utilization of one or more resources by the user to generate first data;
• determining, based on the monitoring, differences between the first data and historical utilization data for the user to determine whether the user's utilization of the one or more resources is anomalous;
• and
• removing, when the user's utilization of the one or more resource is anomalous, the user's access to the one or more resource.