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公开(公告)号：US20200027031A1
公开(公告)日：2020-01-23
申请号：US16039676
申请日：2018-07-19
摘要： 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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公开(公告)号：US12026597B2
公开(公告)日：2024-07-02
申请号：US18158651
申请日：2023-01-24
IPC分类号： H04L67/52 , G06N20/00 , H04L67/50 , H04L101/622
CPC分类号： G06N20/00 , H04L67/535 , H04L67/52 , H04L2101/622
摘要： 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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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分类号： G06N20/00 , H04L67/535 , H04L2101/622
摘要： 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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公开(公告)号：US11586971B2
公开(公告)日：2023-02-21
申请号：US16039676
申请日：2018-07-19
IPC分类号： H04L67/50 , G06N20/00 , H04L101/622 , H04L67/52
摘要： 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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