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公开(公告)号：US11019088B2
公开(公告)日：2021-05-25
申请号：US16886542
申请日：2020-05-28
Applicant: Splunk Inc.
Inventor： Robert Winslow Pratt , Ravi Prasad Bulusu
Abstract: Techniques are described for processing anomalies detected using user-specified rules with anomalies detected using machine-learning based behavioral analysis models to identify threat indicators and security threats to a computer network. In an embodiment, anomalies are detected based on processing event data at a network security system that used rules-based anomaly detection. These rules-based detected anomalies are acquired by a network security system that uses machine-learning based anomaly detection. The rules-based detected anomalies are processed along with machine learning detected anomalies to detect threat indicators or security threats to the computer network. The threat indicators and security threats are output as alerts to the network security system that used rules-based anomaly detection.
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公开(公告)号：US10419465B2
公开(公告)日：2019-09-17
申请号：US16182469
申请日：2018-11-06
Applicant: Splunk Inc.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC: H04L29/06 , G06N99/00 , G06F17/30 , H04L12/26 , H04L12/24 , G06F3/0484 , G06K9/20 , G06N7/00 , G06N5/04 , G06F17/22 , G06N20/00 , G06F16/25 , G06F16/28 , G06F16/44 , G06F16/901 , G06F16/2457 , G06F3/0482 , G06N5/02
Abstract: 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.
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公开(公告)号：US10419463B2
公开(公告)日：2019-09-17
申请号：US15926961
申请日：2018-03-20
Applicant: SPLUNK INC.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC: H04L29/06 , G06N20/00 , G06F16/25 , G06F16/28 , G06F16/44 , G06F16/901 , G06F16/2457 , G06N7/00 , G06F3/0482 , G06K9/20 , G06F3/0484 , H04L12/24 , H04L12/26 , G06F17/22 , G06N5/04 , G06N5/02
Abstract: 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.
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公开(公告)号：US10291635B2
公开(公告)日：2019-05-14
申请号：US15800010
申请日：2017-10-31
Applicant: SPLUNK INC.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC: H04L29/06 , G06N20/00 , G06F16/25 , G06F16/28 , G06F16/44 , G06F16/901 , G06F16/2457 , G06N7/00 , G06F3/0482 , G06K9/20 , G06F3/0484 , H04L12/24 , H04L12/26 , G06F17/22 , G06N5/04
Abstract: 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.
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公开(公告)号：US20180219897A1
公开(公告)日：2018-08-02
申请号：US15926961
申请日：2018-03-20
Applicant: SPLUNK INC.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC: H04L29/06 , G06F3/0482 , H04L12/26 , H04L12/24 , G06N99/00 , G06N7/00 , G06N5/04 , G06K9/20 , G06F17/30 , G06F17/22 , G06F3/0484
CPC classification number: H04L63/1416 , G06F3/0482 , G06F3/0484 , G06F3/04842 , G06F3/04847 , G06F16/24578 , G06F16/254 , G06F16/285 , G06F16/444 , G06F16/9024 , G06F17/2235 , G06K9/2063 , G06N5/022 , G06N5/04 , G06N7/005 , G06N20/00 , 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 , H05K999/99
Abstract: 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.
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Patent Agency Ranking
6.发明申请Sharing Model State Between Real-Time and Batch Paths in Network Security Anomaly Detection 审中-公开在网络安全异常检测中实时和批处理路径之间共享模型状态公开(公告)号：US20170063908A1
公开(公告)日：2017-03-02
申请号：US14929141
申请日：2015-10-30
Applicant: Splunk Inc.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
CPC classification number: 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 , H05K999/99
Abstract: 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.
Abstract translation: 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。
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7.发明申请公开(公告)号：US20170063903A1
公开(公告)日：2017-03-02
申请号：US14928918
申请日：2015-10-30
Applicant: Splunk Inc.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
CPC classification number: H04L63/1416 , G06F3/0482 , G06F3/0484 , G06F3/04842 , G06F3/04847 , G06F17/2235 , G06F17/30061 , G06F17/3053 , G06F17/30563 , G06F17/30598 , G06F17/30958 , G06K9/2063 , G06N5/022 , 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 , H05K999/99
Abstract: 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.
Abstract translation: 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。
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公开(公告)号：US20170063891A1
公开(公告)日：2017-03-02
申请号：US14929224
申请日：2015-10-30
Applicant: Splunk Inc.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
CPC classification number: 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
Abstract: 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.
Abstract translation: 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。
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公开(公告)号：US10911468B2
公开(公告)日：2021-02-02
申请号：US16547235
申请日：2019-08-21
Applicant: Splunk Inc.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC: 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 , G06F17/22 , G06N5/04 , G06N5/02 , G06F40/134
Abstract: 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.
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公开(公告)号：US10904270B2
公开(公告)日：2021-01-26
申请号：US14929187
申请日：2015-10-30
Applicant: Splunk Inc.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu , Marios Iliofotou
IPC: H04L9/00 , H04L29/06 , G06N20/00 , G06F16/25 , G06F16/28 , G06F16/44 , G06F16/901 , G06F16/2457 , H04L12/26 , G06F40/134 , G06N7/00 , G06F3/0482 , G06K9/20 , G06F3/0484 , H04L12/24 , G06N5/04 , G06N5/02
Abstract: 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.
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