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    MALWARE DETECTION SYSTEM ATTACK PREVENTION
    2.
    发明申请
    MALWARE DETECTION SYSTEM ATTACK PREVENTION 审中-公开

    公开(公告)号：US20200028865A1

    公开(公告)日：2020-01-23

    申请号：US16421044

    申请日：2019-05-23

    申请人： Intel Corporation

    发明人： Li Chen

    IPC分类号： H04L29/06 ,  G06N20/00 ,  G06N5/00

    摘要： Systems and methods may be used to prevent attacks on a malware detection system. A method may include modeling a time series of directed graphs using incoming binary files during training of a machine learning system and detecting, during a time-window of the time series, an anomaly based on a directed graph of the time series of directed graphs. The method may include providing an alert that the anomaly has corrupted the machine learning system. The method may include preventing or remedying corruption of the machine learning system.

    ADVERSARIAL ATTACK PREVENTION AND MALWARE DETECTION SYSTEM
    3.
    发明申请
    ADVERSARIAL ATTACK PREVENTION AND MALWARE DETECTION SYSTEM 审中-公开

    公开(公告)号：US20190080089A1

    公开(公告)日：2019-03-14

    申请号：US15700489

    申请日：2017-09-11

    申请人： Intel Corporation

    发明人： Li Chen

    IPC分类号： G06F21/56 ,  G06N99/00

    摘要： 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.

    CONTROL SYSTEMS AND METHODS FOR HEAD-MOUNTED INFORMATION SYSTEMS
    4.
    发明申请
    CONTROL SYSTEMS AND METHODS FOR HEAD-MOUNTED INFORMATION SYSTEMS 审中-公开
    头戴式信息系统的控制系统和方法

    公开(公告)号：US20160202771A1

    公开(公告)日：2016-07-14

    申请号：US15075645

    申请日：2016-03-21

    申请人： Intel Corporation

    发明人： Hamid Abdollahi ,  Dan Eisenhardt ,  Li Chen

    IPC分类号： G06F3/01 ,  G02B27/01 ,  G06F3/0346

    CPC分类号： G06F3/012 ,  A42B3/042 ,  A63B2071/0666 ,  G02B27/017 ,  G02B27/0172 ,  G02B2027/014 ,  G02B2027/0187 ,  G06F3/017 ,  G06F3/0346

    摘要： A head-mounted information system is provided, the head-mounted information system comprising a frame configured to be mounted on a head of a user, a display unit coupled to the frame, a sensor unit coupled to the frame comprising one or more motion sensors, and, a processor unit coupled to the frame and connected to receive signals from the motion sensors. The processor unit comprises a processor and a memory accessible by the processor. The processor unit is configured to monitor the received signals and enter a gesture control mode upon detection of a gesture control enable signal. In the gesture control mode the processor is configured to convert signals received from the motion sensors into menu navigation commands.

    摘要翻译： 提供了一种头戴式信息系统，头戴式信息系统包括被配置为安装在用户的头部上的框架，耦合到框架的显示单元，耦合到框架的传感器单元，该传感器单元包括一个或多个运动传感器 以及耦合到所述帧并被连接以从所述运动传感器接收信号的处理器单元。 处理器单元包括处理器和由处理器可访问的存储器。 处理器单元被配置为在检测到手势控制使能信号时监视所接收的信号并输入手势控制模式。 在手势控制模式中，处理器被配置为将从运动传感器接收的信号转换为菜单导航命令。

    ADVERSARIAL ATTACK PREVENTION AND MALWARE DETECTION SYSTEM
    5.
    发明申请
    ADVERSARIAL ATTACK PREVENTION AND MALWARE DETECTION SYSTEM 有权

    公开(公告)号：US20230119658A1

    公开(公告)日：2023-04-20

    申请号：US18074260

    申请日：2022-12-02

    申请人： Intel Corporation

    发明人： Li Chen

    IPC分类号： G06F21/56 ,  G06N20/00

    摘要： 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.