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61. 发明授权

US10154047B2 Method and system for generating a kill chain for monitoring computer network security 有权
公开(公告)号：US10154047B2
公开(公告)日：2018-12-11
申请号：US14928451
申请日：2015-10-30
申请人： Splunk Inc.
发明人： Sudhakar Muddu , Christos Tryfonas
IPC分类号： H04L29/06 , G06F17/22 , G06F3/0482 , G06F3/0484 , G06N99/00 , G06F17/30 , G06N7/00 , G06K9/20 , H04L12/24 , H04L12/26 , G06N5/04
摘要： A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

62. 发明授权

US10116670B2 Event specific relationship graph generation and application in a machine data processing platform 有权
公开(公告)号：US10116670B2
公开(公告)日：2018-10-30
申请号：US15418546
申请日：2017-01-27
申请人： Splunk Inc.
发明人： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC分类号： H04L29/06 , G06N99/00 , G06F17/30 , G06N7/00 , G06F3/0482 , G06K9/20 , G06F3/0484 , H04L12/24 , H04L12/26 , G06F17/22 , G06N5/04
摘要： A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

63. 发明申请

US20180288079A1 LATERAL MOVEMENT DETECTION FOR NETWORK SECURITY ANALYSIS 审中-公开
公开(公告)号：US20180288079A1
公开(公告)日：2018-10-04
申请号：US15995073
申请日：2018-05-31
申请人： Splunk Inc.
发明人： Sudhakar Muddu , Christos Tryfonas , Fumei Lam , Georgios Apostolopoulos
IPC分类号： H04L29/06 , H04L12/24 , H04L12/26 , G06F3/0482 , G06N99/00 , G06N7/00 , G06N5/04 , G06K9/20 , G06F17/30 , G06F17/22 , G06F3/0484
摘要： A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

64. 发明申请

US20180054452A1 MODEL WORKFLOW CONTROL IN A DISTRIBUTED COMPUTATION SYSTEM 审中-公开
公开(公告)号：US20180054452A1
公开(公告)日：2018-02-22
申请号：US15800000
申请日：2017-10-31
申请人： Splunk Inc.
发明人： Sudhakar Muddu , Christos Tryfonas , Sathyanarayanan Kavacheri
IPC分类号： H04L29/06 , G06N99/00 , H04L12/26 , G06F17/30 , H04L12/24 , G06F3/0484 , G06K9/20 , G06N5/04 , G06F17/22 , G06F3/0482 , G06N7/00
摘要： A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

65. 发明授权

US09813435B2 Network security analysis using real-time and batch detection engines 有权
公开(公告)号：US09813435B2
公开(公告)日：2017-11-07
申请号：US15415747
申请日：2017-01-25
申请人： Splunk Inc.
发明人： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC分类号： H04L29/06 , G06N99/00
CPC分类号： H04L63/1416 , G06F3/0482 , G06F3/0484 , G06F3/04842 , G06F3/04847 , G06F17/2235 , G06F17/30061 , G06F17/3053 , G06F17/30563 , G06F17/30598 , G06F17/30958 , G06K9/2063 , G06N5/04 , G06N7/005 , G06N99/005 , H04L41/0893 , H04L41/145 , H04L41/22 , H04L43/00 , H04L43/045 , H04L43/062 , H04L43/08 , H04L63/06 , H04L63/1408 , H04L63/1425 , H04L63/1433 , H04L63/1441 , H04L63/20 , H04L2463/121
摘要： A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

66. 发明申请

US20170272458A1 Network Security System with Real-Time and Batch Paths 有权
公开(公告)号：US20170272458A1
公开(公告)日：2017-09-21
申请号：US15616889
申请日：2017-06-07
申请人： Splunk Inc.
发明人： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC分类号： H04L29/06 , G06N99/00 , G06N7/00 , G06F3/0484 , H04L12/26 , G06F17/22 , H04L12/24 , G06N5/04 , G06K9/20 , G06F17/30 , G06F3/0482
CPC分类号： H04L63/1416 , G06F3/0482 , G06F3/0484 , G06F3/04842 , G06F3/04847 , G06F17/2235 , G06F17/30061 , G06F17/3053 , G06F17/30563 , G06F17/30598 , G06F17/30958 , G06K9/2063 , G06N5/04 , G06N7/005 , G06N99/005 , H04L41/0893 , H04L41/145 , H04L41/22 , H04L43/00 , H04L43/045 , H04L43/062 , H04L43/08 , H04L63/06 , H04L63/1408 , H04L63/1425 , H04L63/1433 , H04L63/1441 , H04L63/20 , H04L2463/121
摘要： A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

67. 发明申请

US20170063899A1 Interactive Threat Geo-Map for Monitoring Computer Network Security 审中-公开
标题翻译：互动威胁用于监控计算机网络安全的地理图
公开(公告)号：US20170063899A1
公开(公告)日：2017-03-02
申请号：US14928471
申请日：2015-10-30
申请人： Splunk Inc.
发明人： Sudhakar Muddu , Christos Tryfonas
IPC分类号： H04L29/06 , G06F17/30 , G06K9/20 , G06F3/0484
摘要： A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
摘要翻译：安全平台采用多种技术和机制来检测计算机网络环境中的安全相关异常和威胁。安全平台是“大数据”驱动，并采用机器学习来执行安全分析。安全平台执行用户/实体行为分析（UEBA）以检测与安全性相关的异常和威胁，而不管这种异常/威胁是否已知。安全平台可以包括用于检测异常和威胁的实时路径和批处理路径/模式。通过视觉呈现具有风险评级和支持证据的分析结果，安全平台使网络安全管理员能够响应检测到的异常或威胁，并及时采取行动。

68. 发明授权

US09516053B1 Network security threat detection by user/user-entity behavioral analysis 有权
标题翻译：用户/用户实体行为分析的网络安全威胁检测
公开(公告)号：US09516053B1
公开(公告)日：2016-12-06
申请号：US14929168
申请日：2015-10-30
申请人： Splunk Inc.
发明人： Sudhakar Muddu , Christos Tryfonas
IPC分类号： H04L29/06 , G06N99/00
CPC分类号： H04L63/1416 , G06F3/0482 , G06F3/0484 , G06F3/04842 , G06F3/04847 , G06F17/2235 , G06F17/30061 , G06F17/3053 , G06F17/30563 , G06F17/30598 , G06F17/30958 , G06K9/2063 , G06N5/04 , G06N7/005 , G06N99/005 , H04L41/0893 , H04L41/145 , H04L41/22 , H04L43/00 , H04L43/045 , H04L43/062 , H04L43/08 , H04L63/06 , H04L63/1408 , H04L63/1425 , H04L63/1433 , H04L63/1441 , H04L63/20 , H04L2463/121
摘要： A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
摘要翻译：安全平台采用多种技术和机制来检测计算机网络环境中的安全相关异常和威胁。安全平台是“大数据”驱动，并采用机器学习来执行安全分析。安全平台执行用户/实体行为分析（UEBA）以检测与安全性相关的异常和威胁，而不管这种异常/威胁是否已知。安全平台可以包括用于检测异常和威胁的实时路径和批处理路径/模式。通过视觉呈现具有风险评级和支持证据的分析结果，安全平台使网络安全管理员能够响应检测到的异常或威胁，并及时采取行动。

69. 发明授权

US11146574B2 Annotation of event data to include access interface identifiers for use by downstream entities in a distributed data processing system 有权
公开(公告)号：US11146574B2
公开(公告)日：2021-10-12
申请号：US16532312
申请日：2019-08-05
申请人： SPLUNK INC.
发明人： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC分类号： H04L29/06 , G06N20/00 , G06F16/25 , G06F16/28 , G06F16/44 , G06F16/901 , G06F16/2457 , H04L12/26 , G06F40/134 , G06N20/20 , G06N7/00 , G06F3/0482 , G06K9/20 , G06F3/0484 , H04L12/24 , G06N5/04 , G06N5/02
摘要： A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

70. 发明授权

US10587633B2 Anomaly detection based on connection requests in network traffic 有权
公开(公告)号：US10587633B2
公开(公告)日：2020-03-10
申请号：US16050368
申请日：2018-07-31
申请人： Splunk Inc.
发明人： Sudhakar Muddu , Christos Tryfonas , Marios Iliofotou
IPC分类号： H04L9/00 , H04L29/06 , G06N20/00 , G06F16/25 , G06F16/28 , G06F16/44 , G06F16/901 , G06F16/2457 , H04L12/26 , G06N7/00 , G06F3/0482 , G06K9/20 , G06F3/0484 , H04L12/24 , G06N5/04 , G06N5/02
摘要： The disclosed embodiments include a method performed by a computer system. The method includes forming groups of traffic, where each group includes a subset of detected connection requests. The method further includes determining a periodicity of connection requests for each group, identifying a particular group based on whether the periodicity of connection requests of the particular group satisfies a periodicity criterion, determining a frequency of the particular group in the traffic, and identifying the particular group as an anomaly based on whether the frequency of the particular group satisfies a frequency criterion.

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