BEHAVIORAL BIOMETRICS USING KEYPRESS TEMPORAL INFORMATION
Embodiments include a computing device that executes software routines and/or one or more machine-learning architectures including a neural network-based embedding extraction system that to produce an embedding vector representing a user's behavior's keypresses, where the system extracts the behaviorprint embedding vector using the keypress features that the system references later for authenticating users. Embodiments may extract and evaluate keypress features, such as keypress sequences, keypress pressure or volume, and temporal keypress features, such as the duration of keypresses and the interval between keypresses, among others. Some embodiments employ a deep neural network architecture that generates a behaviorprint embedding vector representation of the keypress duration and interval features that is used for enrollment and at inference time to authenticate users.
1 . A computer-implemented method comprising:
obtaining, by a computer, enrollment contact data for an enrollee including enrollment keypress data for an enrollment contact event;
generating, by the computer, a plurality of enrollment keypress features using the keypress data of the enrollment contact data, the plurality of enrollment keypress features including one or more temporal keypress features;
extracting, by a computer, an enrolled behaviorprint vector for the enrollee based upon the plurality of enrollment keypress features, including the one or more enrollment temporal keypress features;
generating, by the computer, a plurality of inbound keypress features using inbound keypress data of an inbound contact data, the plurality of inbound keypress features including one or more inbound temporal keypress features;
extracting, by a computer, an inbound behaviorprint vector for an inbound user based upon the plurality of inbound keypress features, including the one or more inbound temporal keypress features; and
authenticating, by the computer, the inbound user as the enrollee in accordance with an authentication score based upon a distance between the enrolled behaviorprint vector and the inbound behaviorprint vector.
2 . The method according to claim 1 , wherein a temporal keypress feature of the enrollment temporal keypress features or the inbound temporal keypress features includes at least one of a keypress duration or a keypress interval between successive keypress.
3 . The method according to claim 1 , further comprising obtaining, by the computer, inbound keypress data including the inbound keypress features for the inbound contact event via a set of one or more keypress responses corresponding to a set of one or more prompts of an interactive voice response program.
4 . The method according to claim 3 , wherein the keypress data is obtained as one or more dual-tone multi-frequency (DTMF) tones form the interactive voice response program.
5 . The method according to claim 1 , wherein the computer obtains the enrollment contact data for the enrollee including the enrollment keypress data for a plurality of enrollment contact events, and
wherein the computer extracts the enrolled behaviorprint vector for the enrollee based upon the plurality of enrollment keypress features for the plurality of enrollment contact data events.
6 . The method according to claim 1 , further comprising generating, by the computer, a predicted age for the inbound user based upon the inbound temporal keypress features, wherein the computer further authenticates the inbound user as the enrollee based upon comparing the predicted age for the inbound user and an expected age of the enrollee.
7 . The method according to claim 1 , further comprising generating, by the computer, a predicted gender for the inbound user based upon the inbound keypress features, wherein the computer further authenticates the inbound user as the enrollee based upon comparing the predicted gender for the inbound user and an expected gender of the enrollee.
8 . The method according to claim 1 , further comprising:
extracting, by a computer, a robocall behaviorprint vector associated with one or more robocalls based upon a plurality of robocall keypress features of a plurality of robocall contact events, including the one or more robocall temporal keypress features; and
generating, by the computer, a robocall prediction score associated with the inbound contact event based upon a distance between the inbound user behaviorprint vector and the robocall behaviorprint vector.
9 . The method according to claim 8 , wherein extracting the robocall behaviorprint vector includes obtaining, by the computer, a robocall label indicating that a prior contact event is a robocall contact event.
10 . The method according to claim 1 , further comprising:
obtaining, by a computer, training contact data for enrollment keypress data for a plurality of training contact events;
generating, by the computer, a plurality of training keypress features using the training keypress data of the training contact data, the plurality of training keypress features including one or more training temporal keypress features;
extracting, by a computer, an training behaviorprint vector for the training contact event based upon the plurality of training keypress features, including the one or more training temporal keypress features;
generating, by the computer, a predicted output score by applying a machine-learning architecture on the training behaviorprint; and
updating, by the computer, one or more hyperparmeters of the machine-learning architecture according to a loss function determined based upon a distance between the predicted output score and an expected output score.
11 . A system comprising:
a computer comprising a processor configured to:
obtain enrollment contact data for an enrollee including enrollment keypress data for an enrollment contact event;
generate a plurality of enrollment keypress features using the enrollment keypress data of the enrollment contact data, the plurality of enrollment keypress features including one or more enrollment temporal keypress features;
extract an enrolled behaviorprint vector for the enrollee based upon the plurality of enrollment keypress features, including the one or more enrollment temporal keypress features;
generate a plurality of inbound keypress features using inbound keypress data of an inbound contact data, the plurality of inbound keypress features including one or more inbound temporal keypress features;
extract an inbound behaviorprint vector for an inbound user based upon the plurality of inbound keypress features, including the one or more inbound temporal keypress features; and
authenticate the inbound user as the enrollee in accordance with an authentication score based upon a distance between the enrolled behaviorprint vector and the inbound behaviorprint vector.
12 . The system according to claim 11 , wherein a temporal keypress feature of the enrollment temporal keypress features or the inbound temporal keypress features includes at least one of a keypress duration or a keypress interval between successive keypress.
13 . The system according to claim 11 , wherein the computer is further configured to obtain inbound keypress data including the inbound keypress features for the inbound contact event via a set of one or more keypress responses corresponding to a set of one or more prompts of an interactive voice response program.
14 . The system according to claim 13 , wherein the keypress data is obtained as one or more dual-tone multi-frequency (DTMF) tones form the interactive voice response program.
15 . The system according to claim 11 , wherein the computer obtains the enrollment contact data for the enrollee including the enrollment keypress data for a plurality of enrollment contact events, and
wherein the computer extracts the enrolled behaviorprint vector for the enrollee based upon the plurality of enrollment keypress features for the plurality of enrollment contact data events.
16 . The system according to claim 11 , wherein the computer is further configured to generate a predicted age for the inbound user based upon the inbound temporal keypress features, wherein the computer further authenticates the inbound user as the enrollee based upon comparing the predicted age for the inbound user and an expected age of the enrollee.
17 . The system according to claim 11 , wherein the computer is further configured to generate a predicted gender for the inbound user based upon the inbound keypress features, wherein the computer further authenticates the inbound user as the enrollee based upon comparing the predicted gender for the inbound user and an expected gender of the enrollee.
18 . The system according to claim 11 , wherein the computer is further configured to:
extract a robocall behaviorprint vector associated with one or more robocalls based upon a plurality of robocall keypress features of a plurality of robocall contact events, including the one or more robocall temporal keypress features; and
generate a robocall prediction score associated with the inbound contact event based upon a distance between the inbound user behaviorprint vector and the robocall behaviorprint vector.
19 . The system according to claim 18 , wherein when extracting the robocall behaviorprint vector the computer is further configured to obtain a robocall label indicating that a prior contact event is a robocall contact event.
20 . The system according to claim 11 , wherein the computer is further configured to:
obtain training contact data for enrollment keypress data for a plurality of training contact events;
generate a plurality of training keypress features using the training keypress data of the training contact data, the plurality of training keypress features including one or more training temporal keypress features;
extract an training behaviorprint vector for the training contact event based upon the plurality of training keypress features, including the one or more training temporal keypress features;
generate a predicted output score by applying a machine-learning architecture on the training behaviorprint; and
update one or more hyperparmeters of the machine-learning architecture according to a loss function determined based upon a distance between the predicted output score and an expected output score.