Entity Group Behavior Profiling

• US 20150373039A1
• Filed: 06/18/2015
• Published: 12/24/2015
• Est. Priority Date: 06/23/2014
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Niara, Inc. (Hewlett-Packard Enterprise Company)
• Inventors: Wang, Jisheng
• Granted Patent: US 10,212,176 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04W 4/38 for collecting sensor information

Abstract

Entity group behavior profiling. An entity group is created that includes multiple entities, where each entity represents one of a user, a machine, and a service. A behavior profile is created for each one of the entities of the entity group. The behavior of each of one of the entities of the entity group is monitored to detect behavior change. An indicator of compromise is detected based on multiple ones of the entities experiencing substantially a same behavior change.

Claims

1. A method, comprising:

creating an entity group that includes a plurality of entities, wherein each one of the plurality of entities represents one of a user, a machine, and a service;

creating a behavior profile for each one of the plurality of entities of the entity group;

monitoring behavior of each one of the plurality of entities of the entity group to detect behavior change; and

detecting an indicator of compromise based on multiple ones of the plurality of entities experiencing substantially a same behavior change.

2. The method of claim 1, wherein creating the entity group is performed responsive to receiving input from a user that specifies the plurality of entities belonging to the entity group.

3. The method of claim 1, wherein creating the entity group is automatically performed and populated with the plurality of entities based on a set of one or more attributes common to those plurality of entities.

4. The method of claim 1, wherein creating the entity group is automatically performed and populated with the plurality of entities based on those plurality of entities previously showing similar behavior.

5. The method of claim 1, wherein the created behavior profile for each one of the plurality of entities of the entity group includes a set of one or more features that are used to distinguish behavior between the plurality of entities.

6. The method of claim 1, wherein the created behavior profile for each one of the plurality of entities of the entity group includes a set of one or more features that are used to distinguish behavior of the created entity group as compared to behavior of a different entity group.

7. The method of claim 6, wherein the set of features are extracted or derived from metadata and other items of interest including one or more of:

network packets propagating to/from devices, log information, and flow based connection records.

8. The method of claim 7, wherein detecting the indicator of compromise based on multiple ones of the plurality of entities experiencing substantially a same behavior change includes finding a distance change of current behavior versus historical behavior for each of the multiple ones of the plurality of entities that is within a threshold or percentage change.

9. A non-transitory machine-readable storage medium that provides instructions that, if executed by a processor, will cause said processor to perform operations comprising:

creating an entity group that includes a plurality of entities, wherein each one of the plurality of entities represents one of a user, a machine, and a service;

creating a behavior profile for each one of the plurality of entities of the entity group;

monitoring behavior of each one of the plurality of entities of the entity group to detect behavior change; and

detecting an indicator of compromise based on multiple ones of the plurality of entities experiencing substantially a same behavior change.

10. The non-transitory machine-readable storage medium of claim 9, wherein creating the entity group is performed responsive to receiving input from a user that specifies the plurality of entities belonging to the entity group.

11. The non-transitory machine-readable storage medium of claim 9, wherein creating the entity group is automatically performed and populated with the plurality of entities based on a set of one or more attributes common to those plurality of entities.

12. The non-transitory machine-readable storage medium of claim 9, wherein creating the entity group is automatically performed and populated with the plurality of entities based on those plurality of entities previously showing similar behavior.

13. The non-transitory machine-readable storage medium of claim 9, wherein the created behavior profile for each one of the plurality of entities of the entity group includes a set of one or more features that are used to distinguish behavior between the plurality of entities.

14. The non-transitory machine-readable storage medium of claim 9, wherein the created behavior profile for each one of the plurality of entities of the entity group includes a set of one or more features that are used to distinguish behavior of the created entity group as compared to behavior of a different entity group.

15. The non-transitory machine-readable storage medium of claim 14, wherein the set of features are extracted or derived from metadata and other items of interest including one or more of:

network packets propagating to/from devices, log information, and flow based connection records.

16. The non-transitory machine-readable storage medium of claim 15, wherein detecting the indicator of compromise based on multiple ones of the plurality of entities experiencing substantially a same behavior change includes finding a distance change of current behavior versus historical behavior for each of the multiple ones of the plurality of entities that is within a threshold or percentage change.

17. An apparatus for collaborative and adaptive threat intelligence, comprising:

a processor; and

a non-transitory machine-readable storage medium containing instructions executable by said processor whereby said apparatus is operative to;

create an entity group that includes a plurality of entities, wherein each one of the plurality of entities represents one of a user, a machine, and a service;

create a behavior profile for each one of the plurality of entities of the entity group;

monitor behavior of each one of the plurality of entities of the entity group to detect behavior change; and

detect an indicator of compromise based on multiple ones of the plurality of entities experiencing substantially a same behavior change.

18. The non-transitory machine-readable storage medium of claim 17, wherein creation of the entity group is performed responsive to receiving input from a user that specifies the plurality of entities belonging to the entity group.

19. The non-transitory machine-readable storage medium of claim 17, wherein creation of the entity group is automatically performed and populated with the plurality of entities based on a set of one or more attributes common to those plurality of entities.

20. The non-transitory machine-readable storage medium of claim 17, wherein creation of the entity group is automatically performed and populated with the plurality of entities based on those plurality of entities previously showing similar behavior.

21. The non-transitory machine-readable storage medium of claim 17, wherein once created, the behavior profile for each one of the plurality of entities of the entity group includes a set of one or more features that are used to distinguish behavior between the plurality of entities.

22. The non-transitory machine-readable storage medium of claim 17, wherein once crated, the behavior profile for each one of the plurality of entities of the entity group includes a set of one or more features that are used to distinguish behavior of the created entity group as compared to behavior of a different entity group.

23. The non-transitory machine-readable storage medium of claim 22, wherein the set of features are extracted or derived from metadata and other items of interest including one or more of:

network packets propagating to/from devices, log information, and flow based connection records.

24. The non-transitory machine-readable storage medium of claim 23, wherein detection of the indicator of compromise based on multiple ones of the plurality of entities experiencing substantially a same behavior change includes a finding that a distance change of current behavior versus historical behavior for each of the multiple ones of the plurality of entities that is within a threshold or percentage change.