Methods, systems, and devices for dynamically modeling and grouping endpoints for edge networking
Various embodiments described herein disclose an endpoint modeling and grouping management system that can collect data from endpoint computer devices in a network. In some embodiments, agents installed on the endpoints can collect real-time information at the kernel level providing the system with deep visibility. In some embodiments, the endpoint modeling and grouping management system can identify similarities in behavior in response to assessing the data collected by the agents. In some embodiments, the endpoint modeling and grouping management system can dynamically model groups such as logical groups, and cluster endpoints based on the similarities and/or differences in behavior of the endpoints. In some embodiments, the endpoint modeling and grouping management system transmits the behavioral models to the agents to allow the agents to identify anomalies and/or security threats autonomously.
1. A computer-implemented method securing a computer network, the computer-implemented method comprising:
monitoring, by one or more software agents of a plurality of software agents operating on one or more endpoint devices of a plurality of endpoint devices forming the computer network, one or more processes or network communications of a corresponding endpoint device to obtain endpoint data, wherein at least one endpoint device of the plurality of endpoint devices is located outside of a computer network firewall;
transmitting, by the one or more software agents, the endpoint data to a central server;
identifying, by a first software agent of the one or more autonomous software agents or by a central server, one or more anomalous indicators on a corresponding endpoint device based at least in part on the endpoint data, wherein identifying the one or more anomalous indicators comprises comparing activity on the corresponding endpoint device to prior activity of the corresponding endpoint device; and
responding, by the first software agent, to the one or more anomalous indicators on an endpoint-level,
wherein each software agent is capable of identifying the one or more anomalous indicators of the corresponding endpoint device on which the software agent is operating, wherein each software agent is operating on the corresponding endpoint device that is remotely located from the central server,
wherein responding to the one or more anomalous indicators comprises the first autonomous software agent limiting one or more operating system processes or network communications of the corresponding endpoint device.
2. The computer-implemented method of claim 1 , wherein the comparing activity comprises: accessing a rule set, the rule set based on at least in part on the prior activity, and comparing the rule set to the activity on the corresponding endpoint device.
3. The computer-implemented method of claim 1 , wherein the responding by the first software agent is performed without receiving a command instruction from the central server.
4. The computer-implemented method of claim 1 , wherein monitoring the one or more processes or the network communications of the corresponding endpoint device comprises continuously verifying and authenticating the corresponding endpoint device activities.
5. The computer-implemented method of claim 1 , wherein the plurality of endpoint devices comprise one or more cellphones, servers, virtual machines, laptops, tablets, desktop computers, Internet of Things (IoT) devices, landline phones, wearable devices, or smart home devices.
6. The computer-implemented method of claim 1 , wherein the plurality of endpoint devices are grouped into endpoint clusters, and wherein one or more endpoint devices of each endpoint cluster comprise one or more similar processing or network access patterns.
7. The computer-implemented method of claim 1 , wherein each of the plurality of software agents is further configured to perform a point-in-time validation of the corresponding endpoint device, wherein the point-in-time validation comprises a verification that no anomalous indicators are present on the corresponding endpoint device.
8. The computer-implemented method of claim 1 , wherein each of the plurality of software agents is further configured to scan the network to identify devices in or in proximity to the network.
9. The computer-implemented method of claim 8 , wherein the scan comprises utilizing discovery protocols to identify the devices in or in proximity to the network and obtain data therefrom.
10. An edge networking system, the system comprising:
a central server;
a plurality of endpoint devices forming a network and remotely located from the central server, wherein at least one endpoint device of the plurality of endpoint devices is located outside of a computer network firewall; and
a plurality of software agents operating on a, wherein each software agent of the plurality of software agents operates on a corresponding endpoint device and is configured to:
monitor one or more processes or network communications of the corresponding endpoint device to obtain endpoint data;
transmit the endpoint data to the central server;
identify one or more anomalous indicators on the corresponding endpoint device based at least in part on the endpoint data, wherein identifying the one or more anomalous indicators comprises comparing activity on the corresponding endpoint device to prior activity of the corresponding endpoint device; and
respond to the one or more anomalous indicators,
wherein responding to the one or more anomalous indicators comprises limiting one or more operating system processes or network communications of the corresponding endpoint device.
11. The edge networking system of claim 10 , wherein the comparing activity comprises: accessing a rule set, the rule set based on at least in part on the prior activity, and comparing the rule set to the activity on the corresponding endpoint device.
12. The edge networking system of claim 10 , wherein the responding is performed without receiving a command instruction from the central server.
13. The edge networking system of claim 10 , wherein monitoring the one or more processes or the network communications of the corresponding endpoint device comprises continuously verifying and authenticating the corresponding endpoint device activities.
14. The edge networking system of claim 10 , wherein the plurality of endpoint devices comprise one or more cellphones, servers, virtual machines, laptops, tablets, desktop computers, Internet of Things (IoT) devices, landline phones, wearable devices, or smart home devices.
15. The edge networking system of claim 10 , wherein the plurality of endpoint devices are grouped into endpoint clusters, and wherein one or more endpoint devices of each endpoint cluster comprise one or more similar processing or network access patterns.
16. The edge networking system of claim 10 , wherein each of the plurality of software agents is further configured to perform a point-in-time validation of the corresponding endpoint device, wherein the point-in-time validation comprises a verification that no anomalous indicators are present on the corresponding endpoint device.
17. The edge networking system of claim 10 , wherein each of the plurality of software agents is further configured to scan the network to identify devices in or in proximity to the network.
18. The edge networking system of claim 17 , wherein the scan comprises utilizing discovery protocols to identify the devices in or in proximity to the network and obtain data therefrom.