AUTOMATIC DETECTION OF INFORMATION FIELD RELIABILITY FOR A NEW DATA SOURCE

    公开(公告)号:US20180357560A1

    公开(公告)日:2018-12-13

    申请号:US15620116

    申请日:2017-06-12

    CPC classification number: G06N99/005 G06N5/04 H04L43/06

    Abstract: In one embodiment, a device identifies a new data source of characteristics data for a monitored network. The device initiates a quarantine period for the characteristic data from the new data source. The characteristic data from the new data source is quarantined from input to a machine learning-based analyzer during the quarantine period. The device models the characteristic data from the new data source during the quarantine period, to determine whether the characteristic data from the new data source is reliable for input to the machine learning-based analyzer. After the quarantine period, the device provides the characteristic data from the new data source to the machine learning-based analyzer based on a determination that the characteristic data from the new data source is reliable.

    DISTRIBUTED FEEDBACK LOOPS FROM THREAT INTELLIGENCE FEEDS TO DISTRIBUTED MACHINE LEARNING SYSTEMS

    公开(公告)号:US20170279836A1

    公开(公告)日:2017-09-28

    申请号:US15211231

    申请日:2016-07-15

    Abstract: In one embodiment, a device in a network receives anomaly data regarding an anomaly detected by a machine learning-based anomaly detection mechanism of a first node in the network. The device matches the anomaly data to threat intelligence feed data from one or more threat intelligence services. The device determines whether to provide threat intelligence feedback to the first node based on the matched threat intelligence feed data and one or more policy rules. The device provides threat intelligence feedback to the first node regarding the matched threat intelligence feed data, in response to determining that the device should provide threat intelligence feedback to the first node.

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