Generating and deploying security policies for microsegmentation

Patent metadata

Field	Value
Publication number	US-11902145-B2
Application number	US-202217969314-A
Country	US
Kind code	B2
Filing date	Oct 19, 2022
Priority date	Jun 11, 2019
Publication date	Feb 13, 2024
Grant date	Feb 13, 2024

Abstract

Systems and methods include receiving network communication information about hosts in a network and applications executed on the hosts; automatically generating one or more microsegments in the network based on analysis of the obtained network communication information, wherein each microsegment of the one or more microsegments is a grouping of resources including the hosts and the applications executed on the hosts that have rules for network communication; and providing the one or more microsegments to one or more hosts of the hosts, for use by the one or more hosts to allow or block communications locally based on the one or more microsegments. Each of the one or more microsegments can be a grouping of workloads inside a data center.

First claim

What is claimed is:

1. A non-transitory computer-readable storage medium having computer-readable code stored thereon for programming a computing system to perform steps of:
receiving network communication information about hosts in a network and applications executed on the hosts;
analyzing the network communication information to identify server-to-server traffic, application-to-server traffic, and application-to-application traffic;
automatically generating one or more microsegments in the network based on the analyzing, wherein each microsegment of the one or more microsegments is a grouping of resources including the hosts and the applications executed on the hosts that have rules for network communication based on the identified server-to-server traffic, application-to-server traffic, and application-to-application traffic; and
providing the one or more microsegments to one or more hosts of the hosts, for use by the one or more hosts to allow or block communications locally based on the one or more microsegments.

2. The non-transitory computer-readable storage medium of claim 1 , wherein each of the one or more microsegments is a grouping of workloads inside a data center.

3. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further include:
subsequent to deploying the one or more microsegments, observing communication between a plurality of hosts on the network for detecting unassigned communication paths that are either blocked due to no associated microsegment.

4. The non-transitory computer-readable storage medium of claim 3 , wherein the steps further include:
creating a new microsegment for the detected unassigned communication paths.

5. The non-transitory computer-readable storage medium of claim 1 , wherein the automatically generating is based on a trained machine learning model.

6. The non-transitory computer-readable storage medium of claim 5 , wherein the trained machine learning model is trained based on particular network communications labeled as healthy meaning they are permitted and unhealthy meaning they are blocked.

7. The non-transitory computer-readable storage medium of claim 1 , wherein the network communication information includes any of Internet Protocol (IP) addresses, ports, host names, unique identifiers, and application names.

8. The non-transitory computer-readable storage medium of claim 1 , wherein the network communication information includes flow objects with data on both sides of a particular application.

9. The non-transitory computer-readable storage medium of claim 8 , wherein the automatically generating is based on a machine learning model that is trained via unsupervised learning using the flow objects.

10. The non-transitory computer-readable storage medium of claim 1 , wherein each host includes a unique fingerprint.

11. A method comprising steps of:
receiving network communication information about hosts in a network and applications executed on the hosts;
analyzing the network communication information to identify server-to-server traffic, application-to-server traffic, and application-to-application traffic;
automatically generating one or more microsegments in the network based on the analyzing, wherein each microsegment of the one or more microsegments is a grouping of resources including the hosts and the applications executed on the hosts that have rules for network communication based on the identified server-to-server traffic, application-to-server traffic, and application-to-application traffic; and
providing the one or more microsegments to one or more hosts of the hosts, for use by the one or more hosts to allow or block communications locally based on the one or more microsegments.

12. The method of claim 11 , wherein each of the one or more microsegments is a grouping of workloads inside a data center.

13. The method of claim 11 , wherein the steps further include:
subsequent to deploying the one or more microsegments, observing communication between a plurality of hosts on the network for detecting unassigned communication paths that are either blocked due to no associated microsegment.

14. The method of claim 13 , wherein the steps further include:
creating a new microsegment for the detected unassigned communication paths.

15. The method of claim 11 , wherein the automatically generating is based on a trained machine learning model.

16. The method of claim 15 , wherein the trained machine learning model is trained based on particular network communications labeled as healthy meaning they are permitted and unhealthy meaning they are blocked.

17. The method of claim 11 , wherein the network communication information includes any of Internet Protocol (IP) addresses, ports, host names, unique identifiers, and application names.

18. The method of claim 11 , wherein the network communication information includes flow objects with data on both sides of a particular application.

19. The method of claim 18 , wherein the automatically generating is based on a machine learning model that is trained via unsupervised learning using the flow objects.

20. The method of claim 11 , wherein each host includes a unique fingerprint.

Assignees

• Zscaler Inc

Inventors

• Laplante Scott
• Nahas Peter
• Li Xing
• Suresh Suji
• Perkins Daniel R
• Smith Peter

Classifications

• H04L45/02 Primary

  Topology update or discovery · CPC title

• H04L63/10 Primary

  for controlling access to devices or network resources · CPC title

• H04L47/82

  Miscellaneous aspects · CPC title

• H04L63/20

  for managing network security; network security policies in general (filtering policies H04L63/0227) · CPC title

• H04L63/105

  Multiple levels of security · CPC title