Privilege inference and monitoring based on network behavior

• US 10,116,679 B1
• Filed: 05/18/2018
• Issued: 10/30/2018
• Est. Priority Date: 05/18/2018
• Status: Active Grant

• Current Assignee: ExtraHop Networks, Incorporated
• Original Assignee: ExtraHop Networks, Incorporated
• Inventors: Wu, Xue Jun, Chen, Songqian, Kazakova, Olga
• Primary Examiner(s): Mehrmanesh, Amir
• Application Number:
  US15/984,197
• Time in Patent Office: 165 Days
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: H04L 41/046 comprising network management agents or mobile agents therefor
• CPC Class Codes: H04L 41/12 Discovery or management of network topologies
• CPC Class Codes: H04L 43/062 related to network traffic
• CPC Class Codes: H04L 43/12 Network monitoring probes
• CPC Class Codes: H04L 63/102 Entity profiles
• CPC Class Codes: H04L 63/105 Multiple levels of security
• CPC Class Codes: H04L 63/1416 Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04W 12/121 Wireless intrusion detection systems [WIDS]; Wireless intrusion prevention systems [WIPS]

Abstract

Embodiments are directed to monitoring network traffic. A monitoring engine may monitor network traffic associated with entities in one or more networks. A device relation model may be provided based on the entities and the network traffic. An inference engine associate the entities with privilege levels based on the device relation model based on an amount of access or an amount of control that source entities exert over the target entities. An anomaly engine may determine one or more interactions between the source entities and the target entities based on the monitored network traffic. The anomaly engine may generate escalation events based on the interactions associated with the source entities and the target entities where the target entities have a higher privilege level than the source entities. The anomaly engine may provide the escalation events to one or more users.

Claims

1. A method for monitoring network traffic using one or more network computers, wherein execution of instructions by the one or more network computers perform the method comprising:

instantiating a monitoring engine to perform actions, including;

monitoring network traffic associated with a plurality of entities in one or more networks to provide one or more metrics, wherein the entities include one or more of a source entity and one or more of a target entity; and

providing a device relation model based on the plurality of entities, the network traffic, and the one or more metrics; and

instantiating an inference engine to perform actions, including;

associating the plurality of entities with one or more privilege levels based on the device relation model and the one or more metrics, wherein a value for each of the one or more privilege levels is based on one or more of an amount of access or an amount of control that the one or more source entities exert over the one or more target entities; and

increasing the one or more privilege levels for a source entity based on one or more metric values that are associated with the one or more target entities that are linked to the source entity; and

instantiating an anomaly engine to perform actions, including;

determine one or more interactions between the one or more source entities and the one or more target entities based on the monitored network traffic;

generating one or more escalation events based on the one or more interactions and the one or more privilege levels associated with the one or more source entities and the one or more target entities, wherein the one or more interactions or the one or more target entities are associated with a privilege level that exceeds the one or more privilege levels associated with the one or more source entities; and

providing the one or more escalation events to one or more users.

2. The method of claim 1, wherein the anomaly engine performs actions, further comprising:

determining one or more application protocols that are associated with the one or more interactions based on the monitored network traffic; and

determining a privilege level that is associated with each of the one or more interactions that are associated with the one or more determined application protocols.

3. The method of claim 1, wherein the actions of the inference engine further comprise, modifying each privilege level associated with each entity based on one or more characteristics including one or more of resources accessed by an entity, resources provided by the entity, users that access the entity, users that are logged into the entity, an uptime of the entity, privilege levels of connected entities, privilege levels of related entities, privilege levels of the logged in users, number of clients, or additional metadata.

4. The method of claim 1, wherein the actions of the monitoring engine further comprise:

modifying the device relation model based on one or more of additions or removals of the plurality of entities in the network; and

modifying the one or more privilege levels associated with the plurality of entities based on the one or more modifications to the device relation model.

5. The method of claim 1, wherein the anomaly engine performs actions, further comprising:

querying one or more databases to obtain information about the one or more interactions, wherein the one or more databases are separate from the anomaly engine and a network monitoring computer; and

further determining the one or more escalation events based on the obtained information.

6. The method of claim 1, wherein the monitoring engine performs actions, further comprising:

determining one or more network topology characteristics based on the monitored network traffic;

associating one or more default privilege levels with the plurality of entities based on the one or more network topology characteristics.

7. The method of claim 1, wherein the inference engine performs further actions including:

associating the plurality of entities with metadata that includes one or more of user identities, user types, read/write accessibility, application types, direction of relation, age of relation, or frequency of activity; and

modifying the one or more privilege levels based on the metadata.

8. A processor readable non-transitory storage media that includes instructions for monitoring network traffic using one or more network monitoring computers, wherein execution of the instructions by the one or more network computers perform the method comprising:

instantiating a monitoring engine to perform actions, including;

monitoring network traffic associated with a plurality of entities in one or more networks to provide one or more metrics, wherein the entities include one or more of a source entity and one or more of a target entity; and

providing a device relation model based on the plurality of entities, the network traffic, and the one or more metrics; and

instantiating an inference engine to perform actions, including;

associating the plurality of entities with one or more privilege levels based on the device relation model and the one or more metrics, wherein a value for each of the one or more privilege levels is based on one or more of an amount of access or an amount of control that the one or more source entities exert over the one or more target entities; and

increasing the one or more privilege levels for a source entity based on one or more metric values that are associated with the one or more target entities that are linked to the source entity; and

instantiating an anomaly engine to perform actions, including;

determine one or more interactions between the one or more source entities and the one or more target entities based on the monitored network traffic;

generating one or more escalation events based on the one or more interactions and the one or more privilege levels associated with the one or more source entities and the one or more target entities, wherein the one or more interactions or the one or more target entities are associated with a privilege level that exceeds the one or more privilege levels associated with the one or more source entities; and

providing the one or more escalation events to one or more users.

9. The media of claim 8, wherein the anomaly engine performs actions, further comprising:

determining one or more application protocols that are associated with the one or more interactions based on the monitored network traffic; and

determining a privilege level that is associated with each of the one or more interactions that are associated with the one or more determined application protocols.

10. The media of claim 8, wherein the actions of the inference engine further comprise, modifying each privilege level associated with each entity based on one or more characteristics including one or more of resources accessed by an entity, resources provided by the entity, users that access the entity, users that are logged into the entity, an uptime of the entity, privilege levels of connected entities, privilege levels of related entities, privilege levels of the logged in users, number of clients, or additional metadata.

11. The media of claim 8, wherein the actions of the monitoring engine further comprise:

modifying the device relation model based on one or more of additions or removals of the plurality of entities in the network; and

modifying the one or more privilege levels associated with the plurality of entities based on the one or more modifications to the device relation model.

12. The media of claim 8, wherein the anomaly engine performs actions, further comprising:

querying one or more databases to obtain information about the one or more interactions, wherein the one or more databases are separate from the anomaly engine and a network monitoring computer; and

further determining the one or more escalation events based on the obtained information.

13. The media of claim 8, wherein the monitoring engine performs actions, further comprising:

determining one or more network topology characteristics based on the monitored network traffic;

associating one or more default privilege levels with the plurality of entities based on the one or more network topology characteristics.

14. The media of claim 8, wherein the inference engine performs further actions including:

associating the plurality of entities with metadata that includes one or more of user identities, user types, read/write accessibility, application types, direction of relation, age of relation, or frequency of activity; and

modifying the one or more privilege levels based on the metadata.

15. A system for monitoring network traffic in a network:

one or more network computers, comprising;

a transceiver that communicates over the network;

a memory that stores at least instructions; and

one or more processors that execute instructions that perform actions, including;

instantiating a monitoring engine to perform actions, including;

monitoring network traffic associated with a plurality of entities in one or more networks to provide one or more metrics, wherein the entities include one or more of a source entity and one or more of a target entity; and

providing a device relation model based on the plurality of entities, the network traffic, and the one or more metrics; and

instantiating an inference engine to perform actions, including;

associating the plurality of entities with one or more privilege levels based on the device relation model and the one or more metrics, wherein a value for each of the one or more privilege levels is based on one or more of an amount of access or an amount of control that the one or more source entities exert over the one or more target entities; and

increasing the one or more privilege levels for a source entity based on one or more metric values that are associated with the one or more target entities that are linked to the source entity; and

instantiating an anomaly engine to perform actions, including;

determine one or more interactions between the one or more source entities and the one or more target entities based on the monitored network traffic;

generating one or more escalation events based on the one or more interactions and the one or more privilege levels associated with the one or more source entities and the one or more target entities, wherein the one or more interactions or the one or more target entities are associated with a privilege level that exceeds the one or more privilege levels associated with the one or more source entities; and

providing the one or more escalation events to one or more users; and

one or more client computers, comprising;

a transceiver that communicates over the network;

a memory that stores at least instructions; and

one or more processors that execute instructions that perform actions, including;

providing one or more portions of the network traffic.

16. The system of claim 15, wherein the anomaly engine performs actions, further comprising:

determining one or more application protocols that are associated with the one or more interactions based on the monitored network traffic; and

determining a privilege level that is associated with each of the one or more interactions that are associated with the one or more determined application protocols.

17. The system of claim 15, wherein the actions of the inference engine further comprise, modifying each privilege level associated with each entity based on one or more characteristics including one or more of resources accessed by an entity, resources provided by the entity, users that access the entity, users that are logged into the entity, an uptime of the entity, privilege levels of connected entities, privilege levels of related entities, privilege levels of the logged in users, number of clients, or additional metadata.

18. The system of claim 15, wherein the actions of the monitoring engine further comprise:

modifying the device relation model based on one or more of additions or removals of the plurality of entities in the network; and

modifying the one or more privilege levels associated with the plurality of entities based on the one or more modifications to the device relation model.

19. The system of claim 15, wherein the anomaly engine performs actions, further comprising:

querying one or more databases to obtain information about the one or more interactions, wherein the one or more databases are separate from the anomaly engine and a network monitoring computer; and

further determining the one or more escalation events based on the obtained information.

20. The system of claim 15, wherein the monitoring engine performs actions, further comprising:

determining one or more network topology characteristics based on the monitored network traffic;

associating one or more default privilege levels with the plurality of entities based on the one or more network topology characteristics.

21. A network computer for monitoring communication over a network between two or more computers, comprising:

a transceiver that communicates over the network;

a memory that stores at least instructions; and

one or more processors that execute instructions that perform actions, including;

instantiating a monitoring engine to perform actions, including;

monitoring network traffic associated with a plurality of entities in one or more networks to provide one or more metrics, wherein the entities include one or more of a source entity and one or more of a target entity; and

providing a device relation model based on the plurality of entities, the network traffic, and the one or more metrics; and

instantiating an inference engine to perform actions, including;

associating the plurality of entities with one or more privilege levels based on the device relation model and the one or more metrics, wherein a value for each of the one or more privilege levels is based on one or more of an amount of access or an amount of control that the one or more source entities exert over the one or more target entities; and

increasing the one or more privilege levels for a source entity based on one or more metric values that are associated with the one or more target entities that are linked to the source entity; and

instantiating an anomaly engine to perform actions, including;

determine one or more interactions between the one or more source entities and the one or more target entities based on the monitored network traffic;

generating one or more escalation events based on the one or more interactions and the one or more privilege levels associated with the one or more source entities and the one or more target entities, wherein the one or more interactions or the one or more target entities are associated with a privilege level that exceeds the one or more privilege levels associated with the one or more source entities; and

providing the one or more escalation events to one or more users.

22. The network computer of claim 21, wherein the anomaly engine performs actions, further comprising:

determining one or more application protocols that are associated with the one or more interactions based on the monitored network traffic; and

determining a privilege level that is associated with each of the one or more interactions that are associated with the one or more determined application protocols.

23. The network computer of claim 21, wherein the actions of the inference engine further comprise, modifying each privilege level associated with each entity based on one or more characteristics including one or more of resources accessed by an entity, resources provided by the entity, users that access the entity, users that are logged into the entity, an uptime of the entity, privilege levels of connected entities, privilege levels of related entities, privilege levels of the logged in users, number of clients, or additional metadata.

24. The network computer of claim 21, wherein the actions of the monitoring engine further comprise:

modifying the device relation model based on one or more of additions or removals of the plurality of entities in the network; and

modifying the one or more privilege levels associated with the plurality of entities based on the one or more modifications to the device relation model.

25. The network computer of claim 21, wherein the anomaly engine performs actions, further comprising:

querying one or more databases to obtain information about the one or more interactions, wherein the one or more databases are separate from the anomaly engine and a network monitoring computer; and

further determining the one or more escalation events based on the obtained information.

26. The network computer of claim 21, wherein the monitoring engine performs actions, further comprising:

determining one or more network topology characteristics based on the monitored network traffic;

associating one or more default privilege levels with the plurality of entities based on the one or more network topology characteristics.