公开(公告)号：US20220161815A1

公开(公告)日：2022-05-26

申请号：US17434721

申请日：2020-03-27

Applicant: Intel Corporation

Inventor： Petrus J. Van Beek ,  Darshana D. Salvi ,  Mehrnaz Khodam Hazrati ,  Pragya Agrawal ,  Darshan Iyer ,  Suhel Jaber ,  Soila P. Kavulya ,  Hassnaa Moustafa ,  Patricia Ann Robb ,  Naveen Aerrabotu ,  Jeffrey M. Ota ,  Iman Saleh Moustafa ,  Monica Lucia Martinez-Canales ,  Mohamed Eltabakh ,  Cynthia E. Kaschub ,  Rita H. Wouhaybi ,  Fatema S. Adenwala ,  Jithin Sankar Sankaran Kutty ,  Li Chen ,  David J. Zage

IPC: B60W60/00 ,  B60W50/00 ,  G06T9/00

Abstract: According to one embodiment, an apparatus includes an interface to receive sensor data from a plurality of sensors of an autonomous vehicle. The apparatus also includes processing circuitry to apply a sensor abstraction process to the sensor data to produce abstracted scene data, and to use the abstracted scene data in a perception phase of a control process for the autonomous vehicle. The sensor abstraction process may include one or more of: applying a Sensor data response normalization process to the sensor data, applying a warp process to the sensor data, and applying a filtering process to the sensor data.

公开(公告)号：US20220126864A1

公开(公告)日：2022-04-28

申请号：US17434710

申请日：2020-03-27

Applicant: Intel Corporation

Inventor： Hassnaa Moustafa ,  Darshana D. Salvi ,  Suhel Jaber ,  Darshan Iyer ,  Mehrnaz Khodam Hazrati ,  Pragya Agrawal ,  Naveen Aerrabotu ,  Petrus J. Van Beek ,  Monica Lucia Martinez-Canales ,  Patricia Ann Robb ,  Rita Chattopadhyay ,  Jeffrey M. Ota ,  Iman Saleh Moustafa ,  Soila P. Kavulya ,  Karthik Reddy Sripathi ,  Mohamed Eltabakh ,  Igor Tatourian ,  Cynthia E. Kaschub ,  Rita H. Wouhaybi ,  Ignacio J. Alvarez ,  Fatema S. Adenwala ,  Cagri C. Tanriover ,  Maria S. Elli ,  David J. Zage ,  Jithin Sankar Sankaran Kutty ,  Christopher E. Lopez-Araiza ,  Magdiel F. Galán-Oliveras ,  Li Chen ,  Bahareh Sadeghi ,  Subramanian Anandaraj ,  Pradeep Sakhamoori

IPC: B60W60/00 ,  B60W40/09 ,  B60W50/16 ,  H04W4/40 ,  G06N20/00

Abstract: Sensor data is received from a plurality of sensors, where the plurality of sensors includes a first set of sensors and a second set of sensors, and at least a portion of the plurality of sensors are coupled to a vehicle. Control of the vehicle is automated based on at least a portion of the sensor data generated by the first set of sensors. Passenger attributes of one or more passengers within the autonomous vehicles are determined from sensor data generated by the second set of sensors. Attributes of the vehicle are modified based on the passenger attributes and the sensor data generated by the first set of sensors.

公开(公告)号：US11194902B2

公开(公告)日：2021-12-07

申请号：US16234085

申请日：2018-12-27

Applicant: INTEL CORPORATION

Inventor： Li Chen ,  Kai Cong ,  Salmin Sultana

IPC: G06F21/55 ,  G06N20/00

Abstract: The present disclosure is directed to systems and methods of detecting a side-channel attack using hardware counter anomaly detection circuitry to select a subset of HPCs demonstrating anomalous behavior in response to a side-channel attack. The hardware counter anomaly detection circuitry includes data collection circuitry to collect data from a plurality of HPCs, time/frequency domain transform circuitry to transform the collected data to the frequency domain, one-class support vector anomaly detection circuitry to detect anomalous or aberrant behavior by the HPCs. The hardware counter anomaly detection circuitry selects the HPCs having reliable and consistent anomalous activity or behavior in response to a side-channel attack and groups those HPCs into a side-channel attack detection HPC sub-set that may be communicated to one or more external devices.

公开(公告)号：US11188643B2

公开(公告)日：2021-11-30

申请号：US16234144

申请日：2018-12-27

Applicant: Intel Corporation

Inventor： Li Chen ,  Abhishek Basak ,  Salmin Sultana ,  Justin Gottschlich

IPC: G06F21/55 ,  G06N20/00 ,  G06N3/08 ,  G06F21/53

Abstract: Methods, apparatus, systems and articles of manufacture for detecting a side channel attack using hardware performance counters are disclosed. An example apparatus includes a hardware performance counter data organizer to collect a first value of a hardware performance counter at a first time and a second value of the hardware performance counter at a second time. A machine learning model processor is to apply a machine learning model to predict a third value corresponding to the second time. An error vector generator is to generate an error vector representing a difference between the second value and the third value. An error vector analyzer is to determine a probability of the error vector indicating an anomaly. An anomaly detection orchestrator is to, in response to the probability satisfying a threshold, cause the performance of a responsive action to mitigate the side channel anomaly.

公开(公告)号：US10915631B2

公开(公告)日：2021-02-09

申请号：US15922868

申请日：2018-03-15

Applicant: Intel Corporation

Inventor： Li Chen ,  Salmin Sultana

IPC: G06F21/56 ,  G06N3/08 ,  G06K9/62 ,  G06F21/54 ,  G06F21/55 ,  G06K9/32 ,  G06K9/46 ,  G06N3/04 ,  G06N20/10

Abstract: Technologies disclosed herein provide for converting a first data of a first control flow packet to a first pixel, where the first data indicates one or more branches taken during a known execution of an application, generating an array of pixels using the first pixel and one or more other pixels associated with one or more other control flow packets generated from the known execution, transforming the array of pixels into a series of images, and using a machine learning algorithm with inputs to train a behavior model to identify a malicious behavior in an unknown execution of the application. The inputs include one or more images of the series of images and respective image labels assigned to the one or more images. More specific embodiments include extracting the first control flow packet from an execution trace representing at least part of the known execution.

公开(公告)号：US10733294B2

公开(公告)日：2020-08-04

申请号：US15700489

申请日：2017-09-11

Applicant: Intel Corporation

Inventor： Li Chen

IPC: G06F21/56 ,  G06N20/00 ,  G06N7/00

Abstract: Systems and methods may be used to classify incoming testing data, such as binaries, function calls, an application package, or the like, to determine whether the testing data is contaminated using an adversarial attack or benign while training a machine learning system to detect malware. A method may include using a sparse coding technique or a semi-supervised learning technique to classify the testing data. Training data may be used to represent the testing data using the sparse coding technique or to train the supervised portion of the semi-supervised learning technique.

公开(公告)号：US20190272375A1

公开(公告)日：2019-09-05

申请号：US16367611

申请日：2019-03-28

Applicant: Intel Corporation

Inventor： Li Chen

IPC: G06F21/56 ,  G06N3/08

Abstract: There is disclosed in one example an apparatus, including: a hardware platform including a processor and a memory; an image classifier to operate on the hardware platform, the image classifier configured to classify an object under analysis as one of malware or benignware based on an image of the object; and a trust component configured to identify portions of the image that contribute to the classification.

公开(公告)号：US20190220605A1

公开(公告)日：2019-07-18

申请号：US16361397

申请日：2019-03-22

Applicant: Intel Corporation

Inventor： Michael Kounavis ,  Antonios Papadimitriou ,  Anindya Paul ,  Micah Sheller ,  Li Chen ,  Cory Cornelius ,  Brandon Edwards

IPC: G06F21/60 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06F21/60 ,  G06N3/0454 ,  G06N3/08

Abstract: In one example an apparatus comprises a memory and a processor to create, from a first deep neural network (DNN) model, a first plurality of DNN models, generate a first set of adversarial examples that are misclassified by the first plurality of deep neural network (DNN) models, determine a first set of activation path differentials between the first plurality of adversarial examples, generate, from the first set of activation path differentials, at least one composite adversarial example which incorporates at least one intersecting critical path that is shared between at least two adversarial examples in the first set of adversarial examples, and use the at least one composite adversarial example to generate a set of inputs for a subsequent training iteration of the DNN model. Other examples may be described.

公开(公告)号：US20190042745A1

公开(公告)日：2019-02-07

申请号：US15922868

申请日：2018-03-15

Applicant: Intel Corporation

Inventor： Li Chen ,  Salmin Sultana

IPC: G06F21/56 ,  G06N3/08 ,  G06K9/62 ,  G06F21/54

Abstract: Technologies disclosed herein provide for converting a first data of a first control flow packet to a first pixel, where the first data indicates one or more branches taken during a known execution of an application, generating an array of pixels using the first pixel and one or more other pixels associated with one or more other control flow packets generated from the known execution, transforming the array of pixels into a series of images, and using a machine learning algorithm with inputs to train a behavior model to identify a malicious behavior in an unknown execution of the application. The inputs include one or more images of the series of images and respective image labels assigned to the one or more images. More specific embodiments include extracting the first control flow packet from an execution trace representing at least part of the known execution.

公开(公告)号：US20190042743A1

公开(公告)日：2019-02-07

申请号：US15843900

申请日：2017-12-15

Applicant: Intel Corporation

Inventor： Li Chen

IPC: G06F21/56 ,  G06N3/04 ,  G06F15/18

Abstract: An apparatus for computing is presented. In embodiments, the apparatus may include a converter to receive and convert a binary file into a multi-dimensional array, the binary file to be executed on the apparatus or another apparatus. The apparatus may further include an analyzer coupled to the converter, the analyzer to process the multi-dimensional array to detect and classify malware embedded within the multi-dimensional array using at least one partially retrained artificial neural network having an input layer, an output layer and a plurality of hidden layers between the input and output layers. The analyzer may further output a classification result, and the classification result may be is used to prevent execution of the binary file on the apparatus or on another apparatus.