Two-sided machine learning framework for pointer movement-based bot detection

A method comprising:

• obtaining, by a server from an application executable at a user device via a network, a feature representation of movement data corresponding to different locations of a pointing device within a graphical user interface (GUI) of the application, the movement data including a set of coordinates and a timestamp for each of the different locations of the pointing device between consecutive time intervals;
• extracting, by the server from the feature representation, a plurality of features representing a sequence of movements for at least one trajectory of the pointing device through one or more functional areas of the GUI and a duration the pointing device stays within each functional area;
• predicting, by the server using at least one machine learning model and based on the plurality of features, a likelihood that the sequence of movements for the at least one trajectory of the pointing device was produced through human interaction with the pointing device by an actual user of the user device;
• and
• transmitting the prediction from the server to the application at the user device via the network.

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

• tracking, via a graphical user interface (GUI) of an application executable at a user device, a movement of a pointing device;
• obtaining, based on the tracking, movement data associated with different locations of the pointing device within the GUI between consecutive time intervals, the movement data including a set of coordinates and a timestamp for each of the different locations of the pointing device between the consecutive time intervals;
• mapping the movement data to functional areas corresponding to a range of the different locations of the pointing device within the GUI over the consecutive time intervals;
• generating a feature representation of the mapped movement data for at least one trajectory of the pointing device through one or more of the functional areas and a duration the pointing device stays within each functional area;
• and
• determining, using at least one trained machine learning model and based on the generated feature representation, whether the at least one trajectory of the pointing device was produced by human interaction with the pointing device.