Behavioral biometrics and machine learning to secure website logins

A computing system comprising:

• a memory storing program instructions;
• and
• a processing unit coupled to the memory and operable to execute the program instructions, which, when executed by the processing unit, cause the computing system to:
• receive a user-specific first dataset for a first user when the first user performs a first log-in to a website, wherein the first dataset includes the following:
• coordinates of a first pointing device at each time the first pointing device is moved by the first user during the first log-in,
• timestamps associated with pressing and release of each key on a first data input device by the first user during the first log-in,
• timestamps of all clicks of the first pointing device by the first user from beginning to end of the first log-in, and
• a key-specific code of each key being pressed by the first user on the first data input device during the first log-in;
• determine, by the computing system, a total log-in time for the first user based on the beginning and end of the first log-in;
• train a plurality of machine learning-based classifiers in a machine learning (ML) model based on the received user-specific first dataset and on the total log-in time for rejecting a log-in attempt to the website by a second user with log-in credentials of the first user by polling and aggregating each classification generated by each classifier, wherein the second user is different from the first user;
• and
• repeat said receiving and training until each classifier in the ML model attains a corresponding pre-defined level of performance against one or more of the following performance metrics:
• accuracy of a classifier;
• precision of a classifier;
• and
• recall of a classifier.

A computer program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed by a computing system to implement a method comprising:

• receiving a user-specific dataset for a first user when the first user performs a log-in to a website, wherein the dataset includes the following:
• coordinates of a pointing device at each time the pointing device is moved by the first user during the log-in,
• timestamps associated with pressing and release of each key on a data input device by the first user during the log-in,
• timestamps of all clicks of the pointing device by the first user from beginning to end of the log-in, and
• a key-specific American Standard Code for Information Interchange (ASCII) code of each key being pressed by the first user on the data input device during the log-in;
• determining, by the computing system, a total log-in time for the first user based on the beginning and end of the first log-in;
• training a plurality of machine learning-based classifiers in a machine learning (ML) model based on the received user-specific dataset and on the total log-in time for rejecting a log-in attempt to the website by a second user with log-in credentials of the first user by polling and aggregating each classification generated by each classifier, wherein the second user is different from the first user;
• and
• repeating said receiving and training until each classifier in the ML model attains a corresponding pre-defined level of performance against one or more of the following performance metrics:
• accuracy of a classifier;
• precision of a classifier;
• and
• recall of a classifier.