IP Library Granted Patent US 12,326,931
Granted Patent B2
US 12,326,931 · App. 17/362,700 · Granted Jun 10, 2025

Malicious data access as highlighted graph visualization

Inventors: Ahmad Refaat Abdel Fadeel Ahmad El Rouby (Heli, EG); Omar Abdulaal (Mohamed Nagi, EG); Nicole Reineke (Northborough, MA); Joel Christner (San Jose, CA); Farida Shafik (Maadi, EG); Shary Beshara (Cairo, EG)
Assignee: EMC IP Holding Company LLC
G06F21/554G06T11/206G06F2221/034
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Quick Facts
Patent No.
US 12,326,931
App. No.
17/362,700
Granted
Jun 10, 2025
Kind
B2
Abstract

One example method includes monitoring a data access pattern, registering a data access request directed to data, comparing metadata associated with the data access request to a rule, based on a result of the comparing, sending a trigger to a graph service, and using information in the trigger to generate a visual representation of the data access request, wherein the visual representation indicates an extent to which the data access request is considered to constitute a potential threat to the data.

Claims (41)

1. A method, comprising:

monitoring a data access pattern;

registering a data access request that comprises a data access attempt directed to data of a data asset;

generating metadata and an annotation to predict importance, relevance and sensitivity of the data asset relative to the data access request;

comparing the metadata associated with the data access request to a rule;

based on a result of the comparing, sending a trigger to a graph service, wherein the trigger is based on the data access request in combination with another data access request that also concerns the data; and

generating a visual representation of the data access request based in part on information in the trigger, user metadata, group metadata, and asset metadata,

wherein the visual representation indicates an extent to which the data access request is considered to constitute a potential threat to the data,

wherein the visual representation comprises a graph in which data assets and users are denoted by respective nodes,

wherein data access attempts by one or more of the users directed to one or more of the data assets are denoted by respective edges connecting the nodes and the one or more data assets,

wherein each data asset is individually identified by a globally unique identification in the visual representation,

wherein the metadata includes information comprising a file type, a file location, a file size, and a file owner of each data asset, and the annotation includes flags that indicate a type of data found,

wherein edges denoting successful data access attempts are denoted by a first type of visual representation, and

wherein edges denoting risky data access attempts are denoted by a second type of visual representation that is visually distinguishable from the first type of visual representation.

2. The method as recited in claim 1 , wherein the monitoring and the sending are performed by a malicious behavior service.

3. The method as recited in claim 1 , wherein generation of the visual representation of the data access request is performed by the graph service.

4. The method as recited in claim 1 , wherein the metadata comprises metadata of a user who originated the data access request.

5. The method as recited in claim 1 , wherein the user metadata is associated with a user who made the data access request, the group metadata is associated with a group to which the user belongs, and the data asset metadata is associated with the data with which the data access request is concerned.

6. The method as recited in claim 1 , wherein the another data access request is made by a same user that made the data access request or is made by a different user than the user that made the data access request.

7. The method as recited in claim 1 , wherein, in the visual representation, a data access request that is considered a potential threat to the data is visually distinguishable from another data access request that is not considered to be a potential threat to the data.

8. The method as recited in claim 1 , further comprising taking an action regarding the data based on information presented in the visual representation.

9. A non-transitory computer readable storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:

monitoring a data access pattern;

registering a data access request that comprises a data access attempt directed to data of a data asset;

generating metadata and an annotation to predict importance, relevance and sensitivity of the data asset relative to the data access request;

comparing the metadata associated with the data access request to a rule;

based on a result of the comparing, sending a trigger to a graph service, wherein the trigger is based on the data access request in combination with another data access request that also concerns the data; and

generating a visual representation of the data access request based in part on information in the trigger, user metadata, group metadata, and asset metadata, wherein the visual representation indicates an extent to which the data access request is considered to constitute a potential threat to the data,

wherein the visual representation comprises a graph in which data assets and users are denoted by respective nodes,

wherein data access attempts by one or more of the users directed to one or more of the data assets are denoted by respective edges connecting the nodes and the one or more data assets,

wherein each data asset is individually identified by a globally unique identification in the visual representation,

wherein the metadata includes information comprising a file type, a file location, a file size, and a file owner of each data asset, and the annotation includes flags that indicate a type of data found,

wherein edges denoting successful data access attempts are denoted by a first type of visual representation, and

wherein edges denoting risky data access attempts are denoted by a second type of visual representation that is visually distinguishable from the first type of visual representation.

10. The non-transitory computer readable storage medium as recited in claim 9 , wherein the monitoring and the sending are performed by a malicious behavior service.

11. The non-transitory computer readable storage medium as recited in claim 9 , wherein generation of the visual representation of the data access request is performed by the graph service.

12. The non-transitory computer readable storage medium as recited in claim 9 , wherein the metadata comprises metadata of a user who originated the data access request.

13. The non-transitory computer readable storage medium as recited in claim 9 , wherein the user metadata is associated with a user who made the data access request, the group metadata is associated with a group to which the user belongs, and the data asset metadata is associated with the data with which the data access request is concerned.

14. The non-transitory computer readable storage medium as recited in claim 9 , wherein the another data access request is made by a same user that made the data access request or is made by a different user than the user that made the data access request.

15. The non-transitory computer readable storage medium as recited in claim 9 , wherein, in the visual representation, a data access request that is considered a potential threat to the data is visually distinguishable from another data access request that is not considered to be a potential threat to the data.

16. The non-transitory computer readable storage medium as recited in claim 9 , further comprising taking an action regarding the data based on information presented in the visual representation.

Assignments (8)
RELEASE OF SECURITY INTEREST IN PATENTS PREVIOUSLY RECORDED AT REEL/FRAME (057758/0286) Recorded Jun 10, 2022
From: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS NOTES COLLATERAL AGENT
To: DELL PRODUCTS L.P.; EMC IP HOLDING COMPANY LLC
Reel/Frame 061654/0064 →
RELEASE OF SECURITY INTEREST IN PATENTS PREVIOUSLY RECORDED AT REEL/FRAME (058014/0560) Recorded Jun 10, 2022
From: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS NOTES COLLATERAL AGENT
To: DELL PRODUCTS L.P.; EMC IP HOLDING COMPANY LLC
Reel/Frame 062022/0473 →
RELEASE OF SECURITY INTEREST IN PATENTS PREVIOUSLY RECORDED AT REEL/FRAME (057931/0392) Recorded Jun 10, 2022
From: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS NOTES COLLATERAL AGENT
To: DELL PRODUCTS L.P.; EMC IP HOLDING COMPANY LLC
Reel/Frame 062022/0382 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 18, 2022
From: EL ROUBY, AHMAD REFAAT ABDEL FADEEL AHMAD; ABDULAAL, OMAR; REINEKE, NICOLE; CHRISTNER, JOEL; SHAFIK, FARIDA; BESHARA, SHARY
To: EMC IP HOLDING COMPANY
Reel/Frame 059305/0789 →
SECURITY INTEREST Recorded Oct 6, 2021
From: DELL PRODUCTS L.P.; EMC IP HOLDING COMPANY LLC
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS NOTES COLLATERAL AGENT
Reel/Frame 058014/0560 →
SECURITY INTEREST Recorded Oct 6, 2021
From: DELL PRODUCTS L.P.; EMC IP HOLDING COMPANY LLC
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS NOTES COLLATERAL AGENT
Reel/Frame 057931/0392 →
SECURITY INTEREST Recorded Oct 6, 2021
From: DELL PRODUCTS L.P.; EMC IP HOLDING COMPANY LLC
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS NOTES COLLATERAL AGENT
Reel/Frame 057758/0286 →
SECURITY AGREEMENT Recorded Oct 1, 2021
From: DELL PRODUCTS, L.P.; EMC IP HOLDING COMPANY LLC
To: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH
Reel/Frame 057682/0830 →
Continuity (1)
Related Publication 20220414210A1 · Dec 29, 2022
References Cited (97)
US 9038168B2 · Ben-Zvi et al. · 2015 [cited by applicant]
US 9819685B1 · Scott · 2017 [cited by examiner]
US 9967265B1 · Peer · 2018 [cited by examiner]
US 10296520B1 · Ganesh · 2019 [cited by examiner]
US 10541938B1 · Timmerman et al. · 2020 [cited by applicant]
US 10587596B1 · Sahar et al. · 2020 [cited by applicant]
US 10594730B1 · Summers et al. · 2020 [cited by applicant]
US 11256887B1 · Dua et al. · 2022 [cited by applicant]
US 11797702B2 · Reineke et al. · 2023 [cited by applicant]
US 11843622B1 · Tellez et al. · 2023 [cited by applicant]
US 11895126B1 · Satish et al. · 2024 [cited by applicant]
US 11902306B1 · Satish · 2024 [cited by applicant]
US 20040024717A1 · Sneeringer · 2004 [cited by applicant]
US 20040054690A1 · Hillerbrand et al. · 2004 [cited by applicant]
US 20050033803A1 · Vleet et al. · 2005 [cited by applicant]
US 20060114493A1 · Slightam et al. · 2006 [cited by applicant]
US 20080022267A1 · Johnson et al. · 2008 [cited by applicant]
US 20080066165A1 · Rosenoer · 2008 [cited by applicant]
US 20080086482A1 · Weissman · 2008 [cited by applicant]
US 20080195664A1 · Maharajh et al. · 2008 [cited by applicant]
US 20090083652A1 · Krasner et al. · 2009 [cited by applicant]
US 20100286937A1 · Hedley et al. · 2010 [cited by applicant]
US 20110247074A1 · Manring · 2011 [cited by examiner]
US 20110289549A1 · Raup · 2011 [cited by applicant]
US 20120011559A1 · Miettinen et al. · 2012 [cited by applicant]
US 20120041822A1 · Landry et al. · 2012 [cited by applicant]
US 20130173530A1 · Laron · 2013 [cited by examiner]
US 20140006951A1 · Hunter · 2014 [cited by applicant]
US 20140019443A1 · Golshan · 2014 [cited by applicant]
US 20150193781A1 · Dave et al. · 2015 [cited by applicant]
US 20150310188A1 · Ford et al. · 2015 [cited by applicant]
US 20150339324A1 · Westmoreland et al. · 2015 [cited by applicant]
US 20160028758A1 · Ellis · 2016 [cited by examiner]
US 20160248809A1 · Smith et al. · 2016 [cited by applicant]
US 20160292445A1 · Lindemann · 2016 [cited by applicant]
US 20170041296A1 · Ford et al. · 2017 [cited by applicant]
US 20170300690A1 · Ladnai · 2017 [cited by examiner]
US 20180027006A1 · Zimmermann et al. · 2018 [cited by applicant]
US 20180040154A1 · Gibb et al. · 2018 [cited by applicant]
US 20180184171A1 · Danker et al. · 2018 [cited by applicant]
US 20180248974A1 · Zhuang et al. · 2018 [cited by applicant]
US 20190043167A1 · Steyskal et al. · 2019 [cited by applicant]
US 20190080698A1 · Miller · 2019 [cited by applicant]
US 20190213462A1 · McDonald et al. · 2019 [cited by applicant]
US 20190268379A1 · Narayanaswamy et al. · 2019 [cited by applicant]
US 20190319808A1 · Fallah et al. · 2019 [cited by applicant]
US 20190327271A1 · Saxena · 2019 [cited by examiner]
US 20200005168A1 · Bhargava et al. · 2020 [cited by applicant]
US 20200045049A1 · Apostolopoulos · 2020 [cited by examiner]
US 20200145447A1 · Coffey et al. · 2020 [cited by applicant]
US 20200151191A1 · Agrawal et al. · 2020 [cited by applicant]
US 20200220892A1 · Gibson · 2020 [cited by examiner]
US 20200250747A1 · Padmanabhan · 2020 [cited by applicant]
US 20200380147A1 · Kurian · 2020 [cited by applicant]
US 20200412767A1 · Crabtree · 2020 [cited by examiner]
US 20210031106A1 · Alderman et al. · 2021 [cited by applicant]
US 20210056968A1 · Shreeshreemal et al. · 2021 [cited by applicant]
US 20210073179A1 · Berman et al. · 2021 [cited by applicant]
US 20210117923A1 · Gray et al. · 2021 [cited by applicant]
US 20210136115A1 · Andrews et al. · 2021 [cited by applicant]
US 20210136569A1 · Obaidi et al. · 2021 [cited by applicant]
US 20210144159A1 · Sanghvi · 2021 [cited by examiner]
US 20210173718A1 · Patel et al. · 2021 [cited by applicant]
US 20210273961A1 · Humphrey · 2021 [cited by examiner]
US 20210294853A1 · Binkley et al. · 2021 [cited by applicant]
US 20210303714A1 · Yaghoobi et al. · 2021 [cited by applicant]
US 20210385252A1 · Lebin · 2021 [cited by examiner]
US 20220012241A1 · Colcord et al. · 2022 [cited by applicant]
US 20220043927A1 · Sofer · 2022 [cited by examiner]
US 20220067097A1 · Gupta · 2022 [cited by examiner]
US 20220067186A1 · Thakur · 2022 [cited by examiner]
US 20220103566A1 · Faulkner · 2022 [cited by applicant]
US 20220126864A1 · Moustafa et al. · 2022 [cited by applicant]
US 20220161829A1 · McKnew · 2022 [cited by applicant]
US 20220179958A1 · Robison et al. · 2022 [cited by applicant]
US 20220191247A1 · Dhoble et al. · 2022 [cited by applicant]
US 20220210172A1 · Tan · 2022 [cited by examiner]
US 20220237309A1 · Reineke et al. · 2022 [cited by applicant]
US 20220303298A1 · Lee · 2022 [cited by examiner]
US 20220318099A1 · Kotwal · 2022 [cited by examiner]
US 20220384028A1 · Dirghangi et al. · 2022 [cited by applicant]
US 20230004663A1 · Shafik et al. · 2023 [cited by applicant]
US 20230275909A1 · Shivamoggi · 2023 [cited by examiner]
US 20240135004A1 · Adams et al. · 2024 [cited by applicant]
WO 0045286A1 · 2000 [cited by applicant]
WO 2005106658A1 · 2005 [cited by applicant]
WO 2006073543A2 · 2006 [cited by applicant]
WO 2014138984A1 · 2014 [cited by applicant]
Getting Started with Bloodhound. Blog [online]. LBMC Professional Corporation. Nov. 25, 2019 [retrieved on Apr. 30, 2024]. Retrieved from the internet: <URL: https://www.lbmc.com/blog/getting-started-with-bloodhound/> (… [cited by examiner]
ArangoDB, https://www.arangodb.com/. [cited by applicant]
Bonatti, Piero A., Ernesto Damiani, S. De Capitani di Vemercati, and Pierangela Samarati. “A component-based architecture for secure data publication.” In Seventeenth Annual Computer Security Applications Conference, pp… [cited by applicant]
Dia, Ousmane Amadou, and Csilla Farkas. “Risk aware query replacement approach for secure databases performance management.” IEEE Transactions on Dependable and Secure Computing 12, No. 2 (2014): 217-229. (Year: 2014). [cited by applicant]
Gates, Christophers., Ninghui Li, Hao Peng, Bhaskar Sarma, Yuan Qi, Rahul Potharaju, Cristina Nita-Rotaru, and Ian Molloy. “Generating summary risk scores for mobile applications.” IEEE Transactions on dependable and se… [cited by applicant]
Molina, Victor, Marta Kersten-Oertel, and Tristan Glatard. “A conceptual marketplace model for iot generated personal data.” arXiv preprint arXiv:1907.03047 (2019). (Year: 2019). [cited by applicant]
Steve Todd et al., Privacy and Data Confidence in Edge Clouds, White Paper, Network Security, Intel, Oct. 12, 2020, 9, 12, 2020. [cited by applicant]
Tucker. “Privacy pal: improving permission safety awareness of third party applications in on line social networks.” In 2015 IEEE 17th international conference on high performance computing and communications, pp. 1268-… [cited by applicant]
Immonen, Anne, Pekka Paakkonen, and Eila Ovaska. “Evaluating the quality of social media data in big data architecture.” Ieee Access 3 (2015): 2028-2043. (Year: 2015). [cited by applicant]
Cited By (2)
US 12,526,292 US 12,676,869