Techniques and architectures for cross-organization threat detection

    公开(公告)号:US10382463B2

    公开(公告)日:2019-08-13

    申请号:US15385491

    申请日:2016-12-20

    Abstract: Threat detection in a multi-organizational environment. Attribute data corresponding to accesses to a multi-organizational environment and entity data corresponding to accesses to the multi-organizational environment are maintained. A graph based on the attribute data and the entity data where graph edges represent a relationship between an attribute and an entity is generated. Subsequent access are compared to the graph to determine if the subsequent access corresponds to a new relationship. The subsequent access is allowed if the subsequent access does not correspond to a new relationship. The subsequent access further analyzed if the subsequent access corresponds to a new, unexpected relationship.

    In-app behavior-based attack dectection

    公开(公告)号:US10182063B2

    公开(公告)日:2019-01-15

    申请号:US15058954

    申请日:2016-03-02

    Abstract: Architectures and techniques for in-app behavior detection. A behavior detection agent within an application running on a hardware computing device captures events within the application. The events are inputs received from one or more sources external to the application. The behavior detection agent generates an event stream from the captured events. The behavior detection agent analyzes the event stream for significant feature frequencies and associations corresponding to one or more attack profiles. The behavior detection agent initiates an attack response in response to finding one or more significant feature frequencies and associations. The attack response comprises at least changing an operational configuration of the application.

    IN-APP BEHAVIOR-BASED ATTACK DETECTION
    3.
    发明申请

    公开(公告)号:US20170257384A1

    公开(公告)日:2017-09-07

    申请号:US15058954

    申请日:2016-03-02

    CPC classification number: H04L63/1416 G06N99/005 H04L63/1425 H04L63/1441

    Abstract: Architectures and techniques for in-app behavior detection. A behavior detection agent within an application running on a hardware computing device captures events within the application. The events are inputs received from one or more sources external to the application. The behavior detection agent generates an event stream from the captured events. The behavior detection agent analyzes the event stream for significant feature frequencies and associations corresponding to one or more attack profiles. The behavior detection agent initiates an attack response in response to finding one or more significant feature frequencies and associations. The attack response comprises at least changing an operational configuration of the application.

    Feature-Agnostic Behavior Profile Based Anomaly Detection

    公开(公告)号:US20180337937A1

    公开(公告)日:2018-11-22

    申请号:US15600562

    申请日:2017-05-19

    Abstract: Techniques for user behavior anomaly detection. At least one low-variance characteristic is compared to an expected result for the corresponding low-variance characteristics to determine if the low-variance characteristic(s) is/are within a pre-selected range of the expected results. A security response action is taken in response to the low-variance characteristic not being within the first pre-selected range of the expected results. At least one high-variance characteristic is compared to an expected result for the corresponding high-variance characteristics to determine if the high-variance characteristic(s) is/are within a pre-selected range of the expected results. A security response action is taken in response to the high-variance characteristic not being within the first pre-selected range of the expected results. Access is provided if the low-variance and the high-variance characteristics are within the respective expected ranges.

    Feature-Agnostic Behavior Profile Based Anomaly Detection

    公开(公告)号:US20210336980A1

    公开(公告)日:2021-10-28

    申请号:US17316465

    申请日:2021-05-10

    Abstract: Techniques for user behavior anomaly detection. At least one low-variance characteristic is compared to an expected result for the corresponding low-variance characteristics to determine if the low-variance characteristic(s) is/are within a pre-selected range of the expected results. A security response action is taken in response to the low-variance characteristic not being within the first pre-selected range of the expected results. At least one high-variance characteristic is compared to an expected result for the corresponding high-variance characteristics to determine if the high-variance characteristic(s) is/are within a pre-selected range of the expected results. A security response action is taken in response to the high-variance characteristic not being within the first pre-selected range of the expected results. Access is provided if the low-variance and the high-variance characteristics are within the respective expected ranges.

    In-app behavior-based attack detection

    公开(公告)号:US11025652B2

    公开(公告)日:2021-06-01

    申请号:US16247445

    申请日:2019-01-14

    Abstract: Architectures and techniques for in-app behavior detection. A behavior detection agent within an application running on a hardware computing device captures events within the application. The events are inputs received from one or more sources external to the application. The behavior detection agent generates an event stream from the captured events. The behavior detection agent analyzes the event stream for significant feature frequencies and associations corresponding to one or more attack profiles. The behavior detection agent initiates an attack response in response to finding one or more significant feature frequencies and associations. The attack response comprises at least changing an operational configuration of the application.

    Feature-agnostic behavior profile based anomaly detection

    公开(公告)号:US11005864B2

    公开(公告)日:2021-05-11

    申请号:US15600562

    申请日:2017-05-19

    Abstract: Techniques for user behavior anomaly detection. At least one low-variance characteristic is compared to an expected result for the corresponding low-variance characteristics to determine if the low-variance characteristic(s) is/are within a pre-selected range of the expected results. A security response action is taken in response to the low-variance characteristic not being within the first pre-selected range of the expected results. At least one high-variance characteristic is compared to an expected result for the corresponding high-variance characteristics to determine if the high-variance characteristic(s) is/are within a pre-selected range of the expected results. A security response action is taken in response to the high-variance characteristic not being within the first pre-selected range of the expected results. Access is provided if the low-variance and the high-variance characteristics are within the respective expected ranges.

    IN-APP BEHAVIOR-BASED ATTACK DETECTION
    8.
    发明申请

    公开(公告)号:US20190387006A1

    公开(公告)日:2019-12-19

    申请号:US16247445

    申请日:2019-01-14

    Abstract: Architectures and techniques for in-app behavior detection. A behavior detection agent within an application running on a hardware computing device captures events within the application. The events are inputs received from one or more sources external to the application. The behavior detection agent generates an event stream from the captured events. The behavior detection agent analyzes the event stream for significant feature frequencies and associations corresponding to one or more attack profiles. The behavior detection agent initiates an attack response in response to finding one or more significant feature frequencies and associations. The attack response comprises at least changing an operational configuration of the application.

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