Device identifier classification
    1.
    发明授权

    公开(公告)号:US12026597B2

    公开(公告)日:2024-07-02

    申请号:US18158651

    申请日:2023-01-24

    CPC classification number: G06N20/00 H04L67/535 H04L67/52 H04L2101/622

    Abstract: An example method can include tracking, by a network device, a plurality of attributes associated with a plurality of unique client device identifiers stored in a tracking table; deriving, by the network device, a training data set based on the plurality of attributes; and generating, by the network device, a plurality of clusters by inputting the derived training data set to an unsupervised machine learning mechanism. The example method can include receiving, by the network device, a labeling of the plurality of unique client device identifiers in the tracking table based at least on the plurality of clusters; generating, by the network device, a plurality of classifiers by inputting the labelled tracking table to a supervised machine learning mechanism; and classifying, by the network device, a new unique client device identifier in the tracking table based at least on the plurality of classifiers.

    Method and system for database enhancement in a switch

    公开(公告)号:US11423014B2

    公开(公告)日:2022-08-23

    申请号:US16551354

    申请日:2019-08-26

    Abstract: One embodiment of the present invention provides a switch. The switch includes a storage device, a processing module, and a database module. The storage device can maintain a database storing configuration information for the switch. During operation, the processing module produces a piece of data associated with operations of the switch based on the configuration information. The database module then stores the piece of data in a database table of the database without caching the piece of data in a memory of the switch after the piece of data is stored in the database. In this way, the database module can reduce the memory occupancy of the processing module in comparison with the storage occupancy of a schema corresponding to the database table. Subsequently, the processing module can program a hardware module of the switch with the piece of data prior to receiving an acknowledgment from the database module.

    Database operation classification

    公开(公告)号:US11222078B2

    公开(公告)日:2022-01-11

    申请号:US16264923

    申请日:2019-02-01

    Abstract: An example method can include tracking, by a network device, a plurality of database operations performed and a plurality of expected database operations for an event that executes for a time period, generating, by the network device, a plurality of clusters based on a ratio of the database operations performed compared to the plurality of expected database operations and the time period for the event, classifying, by the network device, the clusters based on performance, and evaluating, by the network device, a system performance metric based on a classification of real time data into the clusters.

    Conflict detection in a hybrid network device

    公开(公告)号:US10469349B2

    公开(公告)日:2019-11-05

    申请号:US15327101

    申请日:2015-07-17

    Abstract: A method, system, and computer-readable storage device for detecting conflicts in a hybrid network device is described herein. A hybrid network device may receive a local controller command from a network management device (e.g., a laptop, operated by a network administrator, executing a command line interface). The hybrid network device may convert the local controller command to a software defined command format. The hybrid network device may detect a lack of conflict between the converted local controller command and active flows represented in an active flow repository. Based on the detected lack of conflict, the hybrid network device may update a traffic forwarding table of the hybrid network device in accordance to the local controller command.

    Database operation classification

    公开(公告)号:US11755660B2

    公开(公告)日:2023-09-12

    申请号:US17544078

    申请日:2021-12-07

    CPC classification number: G06F16/906 G06N20/00

    Abstract: An example method can include tracking, by a network device, a plurality of database operations performed and a plurality of expected database operations for an event that executes for a time period, generating, by the network device, a plurality of clusters based on a ratio of the database operations performed compared to the plurality of expected database operations and the time period for the event, classifying, by the network device, the clusters based on performance, and evaluating, by the network device, a system performance metric based on a classification of real time data into the clusters.

    DEVICE IDENTIFIER CLASSIFICATION
    6.
    发明公开

    公开(公告)号:US20230162094A1

    公开(公告)日:2023-05-25

    申请号:US18158651

    申请日:2023-01-24

    CPC classification number: G06N20/00 H04L67/535 H04L2101/622

    Abstract: An example method can include tracking, by a network device, a plurality of attributes associated with a plurality of unique client device identifiers stored in a tracking table; deriving, by the network device, a training data set based on the plurality of attributes; and generating, by the network device, a plurality of clusters by inputting the derived training data set to an unsupervised machine learning mechanism. The example method can include receiving, by the network device, a labeling of the plurality of unique client device identifiers in the tracking table based at least on the plurality of clusters; generating, by the network device, a plurality of classifiers by inputting the labelled tracking table to a supervised machine learning mechanism; and classifying, by the network device, a new unique client device identifier in the tracking table based at least on the plurality of classifiers.

    Device identifier classification
    7.
    发明授权

    公开(公告)号:US11586971B2

    公开(公告)日:2023-02-21

    申请号:US16039676

    申请日:2018-07-19

    Abstract: An example method can include tracking, by a network device, a plurality of attributes associated with a plurality of unique client device identifiers stored in a tracking table; deriving, by the network device, a training data set based on the plurality of attributes; and generating, by the network device, a plurality of clusters by inputting the derived training data set to an unsupervised machine learning mechanism. The example method can include receiving, by the network device, a labeling of the plurality of unique client device identifiers in the tracking table based at least on the plurality of clusters; generating, by the network device, a plurality of classifiers by inputting the labelled tracking table to a supervised machine learning mechanism; and classifying, by the network device, a new unique client device identifier in the tracking table based at least on the plurality of classifiers.

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