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    Knowledge aggregation for GAN-based anomaly detectors
    2.
    发明授权
    Knowledge aggregation for GAN-based anomaly detectors 有权

    公开(公告)号：US10742516B1

    公开(公告)日：2020-08-11

    申请号：US16268853

    申请日：2019-02-06

    Applicant: Cisco Technology, Inc.

    Inventor： Volodymyr Iashyn ,  Borys Viacheslavovych Berlog ,  Dmitri Goloubev

    IPC: G06K9/62 ,  H04L12/24 ,  G06N20/00 ,  H04L29/08

    Abstract: Systems, methods, and computer-readable media for distributing machine learning. In some examples, a first GAN model is deployed to a first network edge device and a second GAN model is deployed to a second network edge device. A generator of the first GAN model can be trained using real telemetry data of a first computing node and a generator of the second GAN model can be trained using real telemetry data of a second IoT device. The generator of the first GAN model and the generator of the second GAN model can be received. Additionally, a unified generator of a unified GAN model can be trained using the generator of the first GAN model and the generator of the second GAN model. Subsequently, the unified GAN model can be deployed to a third computing node for monitoring operation of the third IoT device.

    ANOMALY CLASSIFICATION WITH ATTENDANT WORD ENRICHMENT
    3.
    发明申请
    ANOMALY CLASSIFICATION WITH ATTENDANT WORD ENRICHMENT 有权

    公开(公告)号：US20240370656A1

    公开(公告)日：2024-11-07

    申请号：US18743282

    申请日：2024-06-14

    Applicant: Cisco Technology, Inc.

    Inventor： Dmitri Goloubev ,  Nassim Benoussaid ,  Volodymyr Iashyn ,  Borys Viacheslavovych Berlog ,  Carlos M. Pignataro

    IPC: G06F40/30 ,  G06F16/28 ,  G06F16/35 ,  G06F40/279 ,  G06N20/00

    Abstract: A method includes associating anomalous first text, from a first unstructured data set, with a first classification; processing the first unstructured data set using at least one of ML or AI to identify a second text that is in close context to the first text, and adding the second text to a text list associated with the first classification; enriching the text list by processing the second text to generate a third text, and adding the third text to the text list to produce an enriched text list and such that the third text is also associated with the first classification; matching the text in the enriched text list to text in a second unstructured data set; and classifying the text in the second unstructured data set as having the first classification when the text in the second unstructured data set matches text in the enriched text list.

    KNOWLEDGE AGGREGATION FOR GAN-BASED ANOMALY DETECTORS
    5.
    发明申请
    KNOWLEDGE AGGREGATION FOR GAN-BASED ANOMALY DETECTORS 审中-公开

    公开(公告)号：US20200252296A1

    公开(公告)日：2020-08-06

    申请号：US16268853

    申请日：2019-02-06

    Applicant: Cisco Technology, Inc.

    Inventor： Volodymyr Iashyn ,  Borys Viacheslavovych Berlog ,  Dmitri Goloubev

    IPC: H04L12/24 ,  G06N20/00

    Abstract: Systems, methods, and computer-readable media for distributing machine learning. In some examples, a first GAN model is deployed to a first network edge device and a second GAN model is deployed to a second network edge device. A generator of the first GAN model can be trained using real telemetry data of a first computing node and a generator of the second GAN model can be trained using real telemetry data of a second IoT device. The generator of the first GAN model and the generator of the second GAN model can be received. Additionally, a unified generator of a unified GAN model can be trained using the generator of the first GAN model and the generator of the second GAN model. Subsequently, the unified GAN model can be deployed to a third computing node for monitoring operation of the third IoT device.

    IoT fog as distributed machine learning structure search platform
    6.
    发明授权
    IoT fog as distributed machine learning structure search platform 有权

    公开(公告)号：US11562176B2

    公开(公告)日：2023-01-24

    申请号：US16282781

    申请日：2019-02-22

    Applicant: Cisco Technology, Inc.

    Inventor： Volodymyr Iashyn ,  Gonzalo Salgueiro ,  M. David Hanes

    IPC: G06K9/62 ,  G06N20/00 ,  G06F9/48 ,  H04L67/60

    Abstract: Systems, methods, and computer-readable mediums for distributing machine learning model training to network edge devices, while centrally monitoring training of the models and controlling deployment of the models. A machine learning model architecture can be generated at a machine learning structure controller. The machine learning model architecture can be deployed to network edge devices in a network environment to instantiate and train a machine learning model at the network edge devices. Performance reports indicating performance of the machine learning model at the network edge devices can be received by the machine learning structure controller from the network edge devices. The machine learning structure controller can determine whether to deploy another machine learning model architecture to the network edge devices based on the performance reports and subsequently deploy the another architecture to the network edge devices if it is determined to deploy the architecture based on the performance reports.

    ANOMALY CLASSIFICATION WITH ATTENDANT WORD ENRICHMENT
    7.
    发明申请
    ANOMALY CLASSIFICATION WITH ATTENDANT WORD ENRICHMENT 有权

    公开(公告)号：US20210342543A1

    公开(公告)日：2021-11-04

    申请号：US16914899

    申请日：2020-06-29

    Applicant: Cisco Technology, Inc.

    Inventor： Dmitri Goloubev ,  Nassim Benoussaid ,  Volodymyr Iashyn ,  Borys Viacheslavovych Berlog ,  Carlos M. Pignataro

    IPC: G06F40/30 ,  G06F16/35 ,  G06F16/28 ,  G06N20/00 ,  G06F40/279

    Abstract: A method includes associating anomalous first text, from a first unstructured data set, with a first classification; processing the first unstructured data set using at least one of ML or AI to identify a second text that is in close context to the first text, and adding the second text to a text list associated with the first classification; enriching the text list by processing the second text to generate a third text, and adding the third text to the text list to produce an enriched text list and such that the third text is also associated with the first classification; matching the text in the enriched text list to text in a second unstructured data set; and classifying the text in the second unstructured data set as having the first classification when the text in the second unstructured data set matches text in the enriched text list.

    IOT FOG AS DISTRIBUTED MACHINE LEARNING STRUCTURE SEARCH PLATFORM
    8.
    发明申请
    IOT FOG AS DISTRIBUTED MACHINE LEARNING STRUCTURE SEARCH PLATFORM 审中-公开

    公开(公告)号：US20200272859A1

    公开(公告)日：2020-08-27

    申请号：US16282781

    申请日：2019-02-22

    Applicant: Cisco Technology, Inc.

    Inventor： Volodymyr Iashyn ,  Gonzalo Salgueiro ,  M. David Hanes

    IPC: G06K9/62 ,  G06F9/48 ,  H04L29/08 ,  G06N20/00

    Abstract: Systems, methods, and computer-readable mediums for distributing machine learning model training to network edge devices, while centrally monitoring training of the models and controlling deployment of the models. A machine learning model architecture can be generated at a machine learning structure controller. The machine learning model architecture can be deployed to network edge devices in a network environment to instantiate and train a machine learning model at the network edge devices. Performance reports indicating performance of the machine learning model at the network edge devices can be received by the machine learning structure controller from the network edge devices. The machine learning structure controller can determine whether to deploy another machine learning model architecture to the network edge devices based on the performance reports and subsequently deploy the another architecture to the network edge devices if it is determined to deploy the architecture based on the performance reports.

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