PROCESSING NETWORK DATA USING A GRAPH DATA STRUCTURE

    公开(公告)号:US20180152468A1

    公开(公告)日:2018-05-31

    申请号:US15568280

    申请日:2015-05-28

    Abstract: Certain described examples are directed towards analyzing network data. The network data is processed to generate a graph data structure that has edges that are associated with communication times from the network data and nodes that are associated with computer devices. Representations of the graph data structure are generated over time. Given an indication of at least a computing device, for example as involved in anomalous activity or a security incident, the representations of the graph data structure may be used to determine further associated computer devices that are associated with the indicated device.

    DETERMINING TOPOLOGY USING LOG MESSAGES
    3.
    发明申请

    公开(公告)号:US20180091359A1

    公开(公告)日:2018-03-29

    申请号:US15280940

    申请日:2016-09-29

    CPC classification number: H04L41/069 G06F3/04182 H04L41/12

    Abstract: In some examples, a first pair of parameters in respective first and second log message streams associated with respective first and second source components and a second pair of parameters in the respective first and second log message streams may be identified. The first pair may be identical and the second pair may be identical. It may be determined that first pair of parameters was simultaneously generated and that the second pair of parameters was simultaneously generated in the first and in the second log message streams. A linkage score may be determined between the first and the second source components. The linkage score may be based on the determination that each of the respective first and the second pairs of parameters was simultaneously generated. It may be determined that that the first and second source components are topologically linked based on the linkage score.

    Determining topology using log messages

    公开(公告)号:US10530640B2

    公开(公告)日:2020-01-07

    申请号:US15280940

    申请日:2016-09-29

    Abstract: In some examples, a first pair of parameters in respective first and second log message streams associated with respective first and second source components and a second pair of parameters in the respective first and second log message streams may be identified. The first pair may be identical and the second pair may be identical. It may be determined that first pair of parameters was simultaneously generated and that the second pair of parameters was simultaneously generated in the first and in the second log message streams. A linkage score may be determined between the first and the second source components. The linkage score may be based on the determination that each of the respective first and the second pairs of parameters was simultaneously generated. It may be determined that that the first and second source components are topologically linked based on the linkage score.

    Processing network data using a graph data structure

    公开(公告)号:US10791131B2

    公开(公告)日:2020-09-29

    申请号:US15568280

    申请日:2015-05-28

    Abstract: Certain described examples are directed towards analyzing network data. The network data is processed to generate a graph data structure that has edges that are associated with communication times from the network data and nodes that are associated with computer devices. Representations of the graph data structure are generated over time. Given an indication of at least a computing device, for example as involved in anomalous activity or a security incident, the representations of the graph data structure may be used to determine further associated computer devices that are associated with the indicated device.

    Identifying relationship instances between entities

    公开(公告)号:US10540360B2

    公开(公告)日:2020-01-21

    申请号:US15223271

    申请日:2016-07-29

    Abstract: A method, a computing system, and a non-transitory machine readable storage medium containing instructions for identifying relationships between entities are provided. In an example, the method includes receiving a query. The query specifies a first computing entity, a second computing entity, and a window of time. A data structure is queried based on the query to identify a set of relationship instances each corresponding to a relationship between the first computing entity and the second computing entity during the window of time. A representation of the first computing entity, the second computing entity, and the set of relationship instances is provided at a user interface.

    IDENTIFYING RELATIONSHIP INSTANCES BETWEEN ENTITIES

    公开(公告)号:US20180032588A1

    公开(公告)日:2018-02-01

    申请号:US15223271

    申请日:2016-07-29

    Abstract: A method, a computing system, and a non-transitory machine readable storage medium containing instructions for identifying relationships between entities are provided. In an example, the method includes receiving a query. The query specifies a first computing entity, a second computing entity, and a window of time. A data structure is queried based on the query to identify a set of relationship instances each corresponding to a relationship between the first computing entity and the second computing entity during the window of time. A representation of the first computing entity, the second computing entity, and the set of relationship instances is provided at a user interface.

    DETERMINING TERM SCORES BASED ON A MODIFIED INVERSE DOMAIN FREQUENCY

    公开(公告)号:US20170154107A1

    公开(公告)日:2017-06-01

    申请号:US15325807

    申请日:2014-12-11

    CPC classification number: G06F16/345 G06F16/35 G06F16/36

    Abstract: Determining term scores based on a modified inverse domain frequency is disclosed. One example is a system including a data processing engine, an evaluator, and a data analytics module. The data processing engine identifies a key term associated with a system, and a sub-plurality of a plurality of documents, the sub-plurality of documents associated with the event. The evaluator determines, based on the presence or absence of the key term, a first distribution related to the sub-plurality of documents, and a second distribution related to the plurality of documents, and evaluates, for the key term, a term score based on the first distribution and the second distribution, the term score indicative of a modified inverse domain frequency based on the sub-plurality of documents. The data analytics module includes the key term in a word cloud when the term score for the key term satisfies a threshold.

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