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11.发明申请公开(公告)号：US20190075126A1
公开(公告)日：2019-03-07
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 , G06F3/0482 , G06N7/00 , G06N5/04 , G06F17/22
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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12.发明授权公开(公告)号：US10116670B2
公开(公告)日：2018-10-30
申请号：US15418546
申请日：2017-01-27
Applicant: Splunk Inc.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC: H04L29/06 , G06N99/00 , G06F17/30 , 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：US09813435B2
公开(公告)日：2017-11-07
申请号：US15415747
申请日：2017-01-25
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.
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公开(公告)号：US20170272458A1
公开(公告)日：2017-09-21
申请号：US15616889
申请日：2017-06-07
Applicant: Splunk Inc.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC: H04L29/06 , G06N99/00 , G06N7/00 , G06F3/0484 , H04L12/26 , G06F17/22 , H04L12/24 , G06N5/04 , G06K9/20 , G06F17/30 , G06F3/0482
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.
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公开(公告)号：US11606379B1
公开(公告)日：2023-03-14
申请号：US17236890
申请日：2021-04-21
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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Patent Agency Ranking
公开(公告)号：US11146574B2
公开(公告)日：2021-10-12
申请号：US16532312
申请日：2019-08-05
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 , G06F40/134 , G06N20/20 , 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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17.发明申请公开(公告)号：US20190387007A1
公开(公告)日：2019-12-19
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
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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18.发明授权公开(公告)号：US10419462B2
公开(公告)日：2019-09-17
申请号：US15860049
申请日：2018-01-02
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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19.发明申请公开(公告)号：US20180146000A1
公开(公告)日：2018-05-24
申请号：US15860049
申请日：2018-01-02
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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公开(公告)号：US09900332B2
公开(公告)日：2018-02-20
申请号：US15616889
申请日：2017-06-07
Applicant: Splunk Inc.
Inventor： Sudhakar Muddu , Christos Tryfonas , Ravi Prasad Bulusu
IPC: G06F21/00 , H04L29/06 , G06F17/30 , G06N99/00 , G06N7/00 , G06F3/0484 , G06F3/0482 , G06F17/22 , H04L12/24 , G06N5/04 , G06K9/20 , H04L12/26
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.
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