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    Clustering-based security monitoring of accessed domain names
    1.
    发明授权
    Clustering-based security monitoring of accessed domain names 有权

    公开(公告)号：US11606384B2

    公开(公告)日：2023-03-14

    申请号：US17386989

    申请日：2021-07-28

    Applicant: Splunk Inc.

    Inventor： Munawar Monzy Merza

    IPC: H04L29/06 ,  H04L9/40 ,  A61G17/04 ,  H04L61/4511 ,  A61G17/007 ,  G06F21/50 ,  G06T11/20 ,  H04L67/02

    Abstract: Domain names are determined for each computational event in a set, each event detailing requests or posts of webpages. A number of events or accesses associated with each domain name within a time period is determined. A registrar is further queried to determine when the domain name was registered. An object is generated that includes a representation of the access count and an age since registration for each domain names. A client can interact with the object to explore representations of domain names associated with high access counts and recent registrations. Upon determining that a given domain name is suspicious, a rule can be generated to block access to the domain name.

    Security threat detection based o patterns in machine data events
    2.
    发明授权
    Security threat detection based o patterns in machine data events 有权

    公开(公告)号：US10567412B2

    公开(公告)日：2020-02-18

    申请号：US16100147

    申请日：2018-08-09

    Applicant: SPLUNK INC.

    Inventor： Munawar Monzy Merza ,  John Coates ,  James M Hansen ,  Lucas Murphey ,  David Hazekamp ,  Michael Kinsley ,  Alexander Raitz

    IPC: H04L29/06 ,  G06F21/55 ,  G06F16/2458

    Abstract: A metric value is determined for each event in a set of events that characterizes a computational communication or object. For example, a metric value could include a length of a URL or agent string in the event. A subset criterion is generated, such that metric values within the subset are relatively separated from a population's center (e.g., within a distribution tail). Application of the criterion to metric values produces a subset. A representation of the subset is presented in an interactive dashboard. The representation can include unique values in the subset and counts of corresponding event occurrences. Clients can select particular elements in the representation to cause more detail to be presented with respect to individual events corresponding to specific values in the subset. Thus, clients can use their knowledge system operations and observance of value frequencies and underlying events to identify anomalous metric values and potential security threats.

    Investigative and dynamic detection of potential security-threat indicators from events in big data
    5.
    发明授权
    Investigative and dynamic detection of potential security-threat indicators from events in big data 有权
    从大数据中的事件调查和动态检测潜在的安全威胁指标

    公开(公告)号：US09215240B2

    公开(公告)日：2015-12-15

    申请号：US13956252

    申请日：2013-07-31

    Applicant: Splunk Inc.

    Inventor： Munawar Monzy Merza ,  John Coates ,  James Hansen ,  Lucas Murphey ,  David Hazekamp ,  Michael Kinsley ,  Alexander Raitz

    IPC: H04L29/06 ,  G06F21/55

    CPC classification number: H04L63/1425 ,  G06F17/30551 ,  G06F21/552 ,  G06F2221/2151 ,  H04L63/1408 ,  H04L63/1416

    Abstract: A metric value is determined for each event in a set of events that characterizes a computational communication or object. For example, a metric value could include a length of a URL or agent string in the event. A subset criterion is generated, such that metric values within the subset are relatively separated from a population's center (e.g., within a distribution tail). Application of the criterion to metric values produces a subset. A representation of the subset is presented in an interactive dashboard. The representation can include unique values in the subset and counts of corresponding event occurrences. Clients can select particular elements in the representation to cause more detail to be presented with respect to individual events corresponding to specific values in the subset. Thus, clients can use their knowledge system operations and observance of value frequencies and underlying events to identify anomalous metric values and potential security threats.

    Abstract translation: 为表征计算通信或对象的一组事件中的每个事件确定度量值。 例如，度量值可以包括事件中的URL或代理字符串的长度。 生成子集标准，使得子集内的度量值与群体的中心（例如，分布尾部）相对分开。 将标准应用于度量值产生一个子集。 该子集的表示呈现在交互式仪表板中。 该表示可以包括子集中的唯一值和相应事件发生的计数。 客户端可以选择表示中的特定元素，以便相对于子集中的特定值对应的各个事件来呈现更多的细节。 因此，客户可以使用他们的知识系统操作和遵守价值频率和基础事件来识别异常度量值和潜在的安全威胁。

    IDENTIFYING A CYBER-ATTACK IMPACTING A PARTICULAR ASSET
    6.
    发明申请
    IDENTIFYING A CYBER-ATTACK IMPACTING A PARTICULAR ASSET 有权

    公开(公告)号：US20210029144A1

    公开(公告)日：2021-01-28

    申请号：US17038495

    申请日：2020-09-30

    Applicant: SPLUNK Inc.

    Inventor： Munawar Monzy Merza

    IPC: H04L29/06

    Abstract: In a method, a plurality of events is accessed, wherein an event of the plurality of events includes a portion of raw-machine data from a data source of a plurality of data sources. For at least one event of the plurality of events, a transaction phase of a computer security transaction is correlated with the at least one event based at least in part on a data source associated with the at least one event. The transaction phase of the at least one event is correlated with a particular asset of a plurality of assets.

    Performing rule-based actions based on accessed domain name registrations
    8.
    发明授权
    Performing rule-based actions based on accessed domain name registrations 有权

    公开(公告)号：US10069857B2

    公开(公告)日：2018-09-04

    申请号：US15665372

    申请日：2017-07-31

    Applicant: Splunk Inc.

    Inventor： Munawar Monzy Merza

    IPC: H04L29/06 ,  H04L29/12 ,  H04L29/08

    Abstract: Domain names are determined for each computational event in a set, each event detailing requests or posts of webpages. A number of events or accesses associated with each domain name within a time period is determined. A registrar is further queried to determine when the domain name was registered. An object is generated that includes a representation of the access count and an age since registration for each domain names. A client can interact with the object to explore representations of domain names associated with high access counts and recent registrations. Upon determining that a given domain name is suspicious, a rule can be generated to block access to the domain name.

    DETECTION OF POTENTIAL SECURITY THREATS FROM EVENT DATA
    10.
    发明申请
    DETECTION OF POTENTIAL SECURITY THREATS FROM EVENT DATA 有权
    从事件数据中检测潜在安全威胁

    公开(公告)号：US20160057162A1

    公开(公告)日：2016-02-25

    申请号：US14929321

    申请日：2015-10-31

    Applicant: Splunk Inc.

    Inventor： Munawar Monzy Merza ,  John Coates ,  James M Hansen ,  Lucas Murphey ,  David Hazekamp ,  Michael Kinsley ,  Alexander Raitz

    IPC: H04L29/06

    CPC classification number: H04L63/1425 ,  G06F17/30551 ,  G06F21/552 ,  G06F2221/2151 ,  H04L63/1408 ,  H04L63/1416

    Abstract: A metric value is determined for each event in a set of events that characterizes a computational communication or object. For example, a metric value could include a length of a URL or agent string in the event. A subset criterion is generated, such that metric values within the subset are relatively separated from a population's center (e.g., within a distribution tail). Application of the criterion to metric values produces a subset. A representation of the subset is presented in an interactive dashboard. The representation can include unique values in the subset and counts of corresponding event occurrences. Clients can select particular elements in the representation to cause more detail to be presented with respect to individual events corresponding to specific values in the subset. Thus, clients can use their knowledge system operations and observance of value frequencies and underlying events to identify anomalous metric values and potential security threats.

    Abstract translation: 为表征计算通信或对象的一组事件中的每个事件确定度量值。 例如，度量值可以包括事件中的URL或代理字符串的长度。 生成子集标准，使得子集内的度量值与群体的中心（例如，分布尾部）相对分开。 将标准应用于度量值产生一个子集。 该子集的表示呈现在交互式仪表板中。 该表示可以包括子集中的唯一值和相应事件发生的计数。 客户端可以选择表示中的特定元素，以便相对于子集中的特定值对应的各个事件来呈现更多的细节。 因此，客户可以使用他们的知识系统操作和遵守价值频率和基础事件来识别异常度量值和潜在的安全威胁。

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