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US 11,641,368 B1Granted
Machine learning powered authentication challenges
AssigneeSnap Inc.
InventorsNeil Shah, Mingyi Zhao, Yu-Hsin Chen
Life of this patent
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Abstract
Systems and methods are disclosed for automatically predicting a risk score of a user login attempt by receiving a user login attempt and generating a login feature vector associated with the user login attempt. The systems and methods further train a machine learning technique to establish a relationship between the login feature vector and the risk score. The trained machine learning technique is applied to new user login attempts to predict a risk score associated with the login attempt and issue an authentication challenge to the user if the risk score exceeds a predetermined threshold value.
Claims20
A computer-implemented method comprising:
- receiving, by one or more processors, a user login attempt, the user login attempt associated with a user of a client device and a login source;
- generating a login feature vector associated with the user login attempt, the login feature vector comprising a plurality of user-context features that represent a probability of malicious software attacks associated with the user, login-context features that represent a probability of malicious software attacks associated with the login-source, and binary signals that represent a probability of malicious software attacks associated with the user login attempt;
- associating the plurality of user-context features with a plurality of respective risk values;
- determining a risk score associated with the user login attempt based on the plurality of respective risk values using a trained machine learning model;
- determining that the risk score exceeds a predetermined threshold value;
- in response to the determination that the risk score exceeds the predetermined threshold value, issuing an authentication challenge to the user;
- and
- associating a challenge response label with the login feature vector based on a user response to the authentication challenge.
5 more elements
A system comprising:
- a memory;
- and
- at least one hardware processor coupled to the memory and comprising instructions that cause the system to perform operations comprising:
- receiving, by one or more processors, a user login attempt, the user login attempt associated with a user of a client device and a login source;
- generating a login feature vector associated with the user login attempt, the login feature vector comprising a plurality of user-context features that represent a probability of malicious software attacks associated with the user, login-context features that represent a probability of malicious software attacks associated with the login-source, and binary signals that represent a probability of malicious software attacks associated with the user login attempt;
- associating the plurality of user-context features with a plurality of respective risk values;
- determining a risk score associated with the user login attempt based on the plurality of respective risk values using a trained machine learning model;
- determining that the risk score exceeds a predetermined threshold value;
- in response to the determination that the risk score exceeds the predetermined threshold value, issuing an authentication challenge to the user;
- and
- associating a challenge response label with the login feature vector based on a user response to the authentication challenge.
8 more elements
A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine cause the machine to perform operations comprising:
- receiving, by one or more processors, a user login attempt, the user login attempt associated with a user of a client device and a login source;
- generating a login feature vector associated with the user login attempt, the login feature vector comprising a plurality of user-context features that represent a probability of malicious software attacks associated with the user, login-context features that represent a probability of malicious software attacks associated with the login-source, and binary signals that represent a probability of malicious software attacks associated with the user login attempt;
- associating the plurality of user-context features with a plurality of respective risk values;
- determining a risk score associated with the user login attempt based on the plurality of respective risk values using a trained machine learning model;
- determining that the risk score exceeds a predetermined threshold value;
- in response to the determination that the risk score exceeds the predetermined threshold value, issuing an authentication challenge to the user;
- and
- associating a challenge response label with the login feature vector based on a user response to the authentication challenge.
5 more elements
Dependencies are inferred from the claim text.