公开(公告)号：US20240171558A1

公开(公告)日：2024-05-23

申请号：US17989284

申请日：2022-11-17

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Antoine Diffloth ,  Prince Paulraj ,  James Pratt

IPC: H04L9/40 ,  G06F21/31

CPC classification number: H04L63/08 ,  G06F21/316 ,  H04L2463/082

Abstract: A processing system including at least one processor may obtain a first input data set associated with a telephone number from a first service provider that implements a multi-factor authentication process for permitting an access to a service of the first service provider and may apply at least the first input data set to a machine learning model implemented by the processing system to obtain a risk score associated with the telephone number for a subscriber identity module swap of a subscriber identity module, where the machine learning model is trained to generate the risk score associated with the telephone number in accordance with at least the first input data set. The processing system may then perform at least one remedial action associated with the telephone number and the subscriber identity module, in response to the risk score.

公开(公告)号：US12095813B2

公开(公告)日：2024-09-17

申请号：US17380677

申请日：2021-07-20

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Antoine Diffloth ,  Natalie Gilbert ,  Sundaresan Manoharan

IPC: H04L9/40 ,  G06F18/2413 ,  G06N3/04 ,  H04L61/4511

CPC classification number: H04L63/1483 ,  G06F18/24147 ,  G06N3/04 ,  H04L61/4511 ,  H04L63/1416 ,  H04L63/1425 ,  H04L63/1475

Abstract: The described technology is generally directed towards homoglyph attack detection. A homoglyph attack detection service can create images of customer's protected domain names. A convolutional neural network can generate feature vectors based on the images. The feature vectors can be stored in a similarity search data store. Newly observed domain names can be compared to the customer's protected domain names, by also generating feature vectors for the newly observed domain names and conducting approximate nearest neighbor searches. Search results can be further evaluated by comparing protected domain names to newly observed domain names using a siamese neural network which applies a similarity threshold. Newly observed domain names that meet or exceed the similarity threshold can be flagged for further action.

公开(公告)号：US20230028490A1

公开(公告)日：2023-01-26

申请号：US17380677

申请日：2021-07-20

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Antoine Diffloth ,  Natalie Gilbert ,  Sundaresan Manoharan

IPC: H04L29/06 ,  H04L29/12 ,  G06K9/62 ,  G06N3/04

Abstract: The described technology is generally directed towards homoglyph attack detection. A homoglyph attack detection service can create images of customer's protected domain names. A convolutional neural network can generate feature vectors based on the images. The feature vectors can be stored in a similarity search data store. Newly observed domain names can be compared to the customer's protected domain names, by also generating feature vectors for the newly observed domain names and conducting approximate nearest neighbor searches. Search results can be further evaluated by comparing protected domain names to newly observed domain names using a siamese neural network which applies a similarity threshold. Newly observed domain names that meet or exceed the similarity threshold can be flagged for further action.

公开(公告)号：US20240414199A1

公开(公告)日：2024-12-12

申请号：US18813106

申请日：2024-08-23

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Antoine Diffloth ,  Natalie Gilbert ,  Sundaresan Manoharan

IPC: H04L9/40 ,  G06F18/2413 ,  G06N3/04 ,  H04L61/4511

Abstract: The described technology is generally directed towards homoglyph attack detection. A homoglyph attack detection service can create images of customer's protected domain names. A convolutional neural network can generate feature vectors based on the images. The feature vectors can be stored in a similarity search data store. Newly observed domain names can be compared to the customer's protected domain names, by also generating feature vectors for the newly observed domain names and conducting approximate nearest neighbor searches. Search results can be further evaluated by comparing protected domain names to newly observed domain names using a siamese neural network which applies a similarity threshold. Newly observed domain names that meet or exceed the similarity threshold can be flagged for further action.

公开(公告)号：US20240095579A1

公开(公告)日：2024-03-21

申请号：US17949787

申请日：2022-09-21

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Prince Paulraj ,  Antoine Diffloth ,  James Pratt

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A processing system including at least one processor may obtain a request from a first entity to train a machine learning model, access at least one data feature of at least a second entity, and train the machine learning model on behalf of the first entity in accordance with the at least one data feature of the at least the second entity to generate a trained machine learning model, where the at least one data feature of the at least the second entity is a restricted data feature that is inaccessible to the first entity. The processing system may then provide the trained machine learning model to the first entity.

公开(公告)号：US20220292999A1

公开(公告)日：2022-09-15

申请号：US17201672

申请日：2021-03-15

Applicant: AT&T Intellectual Property I, L.P. ,  AT&T Mobility II LLC

Inventor： Matthew Kratzer ,  Gary Gadson ,  Justin Hone ,  Sean Griffith ,  Christopher Rebacz ,  Jonathan Remington ,  Antoine Diffloth ,  Arvind Shankar Prabhakar ,  Qiuying Jiang ,  Michael Lemen ,  Michael Maher ,  Aaron Thomas ,  David E. Patterson

IPC: G09B7/04 ,  G06Q10/06 ,  G06N20/00 ,  G06Q50/20

Abstract: Aspects of the subject disclosure may include, for example, receiving employee performance data for a group of employees including a particular employee, the employee performance data including particular performance data for the particular employee, the employee performance data associated with key performance indicator (KPIs) for the group of employees including a particular KPI associated with the a task performed by the particular employee; determining, for a plurality of training courses, a probability of each training course being associated with improved performance by the group of employees for each KPI of the KPIs; producing a probability distribution and a confidence score, recommending, based on the probability distribution, one or more recommended training courses for the employee; exploring, based on the confidence score, training courses of the plurality of training courses having a relatively low confidence score; receiving subsequent performance data for a time period following completion of the one or more recommended training courses by the particular employee; evaluating effectiveness of the one or more training courses based on the subsequent performance data; and modifying at least one recommended training course of the one or more recommended training courses, wherein the modifying is responsive to the evaluating effectiveness. Other embodiments are disclosed.