公开(公告)号：US20240211549A1

公开(公告)日：2024-06-27

申请号：US18591640

申请日：2024-02-29

Applicant: Intel Corporation

Inventor： Marius Arvinte ,  Brandon Edwards ,  Cory Cornelius ,  Jason Martin ,  Sebastian Szyller ,  Micah Sheller ,  Nageen Himayat

IPC: G06F21/10

CPC classification number: G06F21/101

Abstract: An example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to access a first set of samples associated with a diffusion model, the first set of samples including a plurality of input data samples, generate a representation of the first set of samples, sample the representation of the first set of samples to generate a representation of a second set of samples, and generate the second set of samples from the representation of the second set of samples, the second set of samples including a plurality of output data samples, an output data sample corresponding to an input data sample and being different from the corresponding input data sample.

公开(公告)号：US20180097822A1

公开(公告)日：2018-04-05

申请号：US15283389

申请日：2016-10-01

Applicant: Intel Corporation

Inventor： Yonghong Huang ,  Jason Martin ,  Micah J. Sheller ,  Cory Cornelius ,  Shih-han Wang

IPC: H04L29/06 ,  G06N3/04 ,  G06N3/08

CPC classification number: H04L63/1408 ,  G06F21/562 ,  G06F21/57 ,  G06N99/005

Abstract: Technologies for analyzing a Uniform Resource Locator (URL) include a multi-stage URL analysis system. The multi-stage URL analysis system analyzes the URL using a multi-stage analysis. In the first stage, the multi-stage URL analysis system analyzes the URL using an ensemble lexical analysis. In the second stage, the multi-stage URL analysis system analyzes the URL based on third-party detection results. In the third stage, the multi-stage URL analysis system analyzes the URL based on metadata related to the URL. The multi-stage URL analysis system advances the stages of analysis if a malicious classification score determined by each stage does not satisfy a confidence threshold. The URL may also be selected for additional rigorous analysis using selection criteria not used in by the analysis stages.

公开(公告)号：US20250061229A1

公开(公告)日：2025-02-20

申请号：US18938955

申请日：2024-11-06

Applicant: Intel Corporation

Inventor： Micah Sheller ,  Cory Cornelius

IPC: G06F21/62 ,  G06F16/951 ,  G06F18/21 ,  G06F18/2411 ,  G06F21/74 ,  G06N3/04 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N20/00 ,  G06N99/00 ,  H04L67/10

Abstract: Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

公开(公告)号：US20230205918A1

公开(公告)日：2023-06-29

申请号：US18070299

申请日：2022-11-28

Applicant: INTEL CORPORATION

Inventor： Micah Sheller ,  Cory Cornelius

IPC: G06F21/62 ,  H04L67/10 ,  G06N20/00 ,  G06F16/951 ,  G06N3/063 ,  G06F21/74 ,  G06N3/08 ,  G06N99/00 ,  G06N3/04 ,  G06F18/21 ,  G06F18/2411 ,  G06N3/045

CPC classification number: G06F21/6245 ,  H04L67/10 ,  G06N20/00 ,  G06F16/951 ,  G06N3/063 ,  G06F21/74 ,  G06N3/08 ,  G06N99/00 ,  G06N3/04 ,  G06F18/21 ,  G06F18/2411 ,  G06N3/045

Abstract: Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

公开(公告)号：US20200327238A1

公开(公告)日：2020-10-15

申请号：US16910722

申请日：2020-06-24

Applicant: INTEL CORPORATION

Inventor： Shih-Han Wang ,  Yonghong Huang ,  Micah Sheller ,  Cory Cornelius

IPC: G06F21/57 ,  G06N5/04 ,  G06F21/56 ,  G06N20/00 ,  G06N3/08 ,  G06F21/55 ,  G06N3/00 ,  G06N3/04

Abstract: Embodiments discussed herein may be generally directed to systems and techniques to generate a quality score based on an observation and an action caused by an actor agent during a testing phase. Embodiments also include determining a temporal difference between the quality score and a previous quality score based on a previous observation and a previous action, determining whether the temporal difference exceeds a threshold value, and generating an attack indication in response to determining the temporal difference exceeds the threshold value.

公开(公告)号：US10726134B2

公开(公告)日：2020-07-28

申请号：US16103137

申请日：2018-08-14

Applicant: INTEL CORPORATION

Inventor： Shih-Han Wang ,  Yonghong Huang ,  Micah Sheller ,  Cory Cornelius

IPC: G06F21/57 ,  G06N5/04 ,  G06F21/56 ,  G06N20/00 ,  G06N3/08 ,  G06F21/55 ,  G06N3/00 ,  G06N3/04

Abstract: Embodiments discussed herein may be generally directed to systems and techniques to generate a quality score based on an observation and an action caused by an actor agent during a testing phase. Embodiments also include determining a temporal difference between the quality score and a previous quality score based on a previous observation and a previous action, determining whether the temporal difference exceeds a threshold value, and generating an attack indication in response to determining the temporal difference exceeds the threshold value.

公开(公告)号：US11501001B2

公开(公告)日：2022-11-15

申请号：US16910722

申请日：2020-06-24

Applicant: INTEL CORPORATION

Inventor： Shih-Han Wang ,  Yonghong Huang ,  Micah Sheller ,  Cory Cornelius

IPC: G06F21/57 ,  G06N5/04 ,  G06F21/56 ,  G06N20/00 ,  G06N3/08 ,  G06F21/55 ,  G06N3/00 ,  G06N3/04

Abstract: Embodiments discussed herein may be generally directed to systems and techniques to generate a quality score based on an observation and an action caused by an actor agent during a testing phase. Embodiments also include determining a temporal difference between the quality score and a previous quality score based on a previous observation and a previous action, determining whether the temporal difference exceeds a threshold value, and generating an attack indication in response to determining the temporal difference exceeds the threshold value.

公开(公告)号：US10749863B2

公开(公告)日：2020-08-18

申请号：US15439224

申请日：2017-02-22

Applicant: Intel Corporation

Inventor： Cory Cornelius ,  Jason Martin ,  Ramune Nagisetty ,  Micah J. Sheller ,  Thao W. Xiong ,  Reese Bowes

IPC: H04L29/06 ,  G06F1/16 ,  H04W12/06 ,  G06K9/00 ,  H04W12/00

Abstract: In one embodiment, an apparatus includes: a bioimpedance sensor to generate bioimpedance information based on bioimpedance sample information from at least some of a plurality of electrodes to be adapted about a portion of a person; at least one biometric sensor to generate biometric information based on biometric sample information from at least some of the plurality of electrodes; at least one environmental sensor to generate environmental context data; and an integration circuit to receive the bioimpedance information, the biometric information and the environmental context data and to adjust the bioimpedance information based at least in part on a value of one or more of the biometric information and the environmental context data. Other embodiments are described and claimed.

公开(公告)号：US10218716B2

公开(公告)日：2019-02-26

申请号：US15283389

申请日：2016-10-01

Applicant: Intel Corporation

Inventor： Yonghong Huang ,  Jason Martin ,  Micah J. Sheller ,  Cory Cornelius ,  Shih-han Wang

IPC: H04L29/06 ,  G06N99/00 ,  G06F21/56 ,  G06F21/57

Abstract: Technologies for analyzing a Uniform Resource Locator (URL) include a multi-stage URL analysis system. The multi-stage URL analysis system analyzes the URL using a multi-stage analysis. In the first stage, the multi-stage URL analysis system analyzes the URL using an ensemble lexical analysis. In the second stage, the multi-stage URL analysis system analyzes the URL based on third-party detection results. In the third stage, the multi-stage URL analysis system analyzes the URL based on metadata related to the URL. The multi-stage URL analysis system advances the stages of analysis if a malicious classification score determined by each stage does not satisfy a confidence threshold. The URL may also be selected for additional rigorous analysis using selection criteria not used in by the analysis stages.

公开(公告)号：US09934372B1

公开(公告)日：2018-04-03

申请号：US15476995

申请日：2017-04-01

Applicant: Intel Corporation

Inventor： Cory Cornelius ,  Micah J. Sheller ,  Jason Martin

IPC: G06F21/32 ,  A61B5/05 ,  A61B5/053 ,  A61B5/117 ,  A61B5/00

CPC classification number: G06F21/32 ,  A61B5/0024 ,  A61B5/0536 ,  A61B5/117 ,  A61B5/6803

Abstract: Technologies for performing orientation-independent bioimpedance-based user authentication include a compute device. The compute device includes a plurality of electrodes usable to transmit an alternating current and measure a bioimpedance in a section of the body of a user. The compute device is to transmit, with a pair of the electrodes, an alternating current through the section of the body of the user, measure, with a pair of the electrodes, a bioimpedance of the section of the body to the transmitted alternating current, generate a tomographic image as a function of the measured bioimpedance, identify a position of a fiduciary marker in the tomographic image, rotate the tomographic image to a predefined orientation as a function of the position of the fiduciary marker, extract one or more biometric features from the rotated tomographic image, and perform authentication of the user as a function of the extracted one or more biometric features.