A distributed security system can include instances of a compute engine that can execute either locally in security agents on client devices or as cloud instances in a security network. Event data can be processed by elements of the distributed security system according to centrally-defined ontological definitions and/or configurations. Bounding managers of local security agents can control how much event data is sent to the security network. A storage engine in the security network can store event data received from client devices, can route event data to other elements of the security network, including cloud instances of the compute engine. An experimentation engine of the security network can also at least temporarily adjust other elements of the distributed security system during experiments or tests.

Methods and systems for classifying a device on a network. The systems and methods may receive network activity data associated with an unknown device. A classifier executing one or more machine learning models may then classify the device as an internet of things (IoT) device or a non-IoT device.

Methods and systems for classifying a device on a network. The systems and methods may receive network activity data associated with an unknown device. A classifier executing one or more machine learning models may then classify the device as an internet of things (IoT) device or a non-IoT device.

The present invention discloses a method and control system for monitoring plurality of equipment in industrial plant connected over SNMP based network. Each of plurality of equipment is associated with a server which is communicatively connected with client. One or more equipment which are capable of reporting monitoring objects are identified using a configuration file. Further, polling for monitoring objects based on capability data associated with one or more equipment is performed. Secure configuration of plurality of equipment in SNMP based network is achieved. Management of network traffic is performed. Unauthorized extraction of monitoring objects is reduced in the network.

The present invention discloses a method and control system for monitoring plurality of equipment in industrial plant connected over SNMP based network. Each of plurality of equipment is associated with a server which is communicatively connected with client. One or more equipment which are capable of reporting monitoring objects are identified using a configuration file. Further, polling for monitoring objects based on capability data associated with one or more equipment is performed. Secure configuration of plurality of equipment in SNMP based network is achieved. Management of network traffic is performed. Unauthorized extraction of monitoring objects is reduced in the network.

Controller devices may be configured to automatically connect to a remote management server (e.g., a “cloud”-based management server), and may offload received data and analyses to the remote management server via wired or wireless communications. The controller devices may further communicate with the management server, user computing devices, and/or human machine interface devices, e.g., to provide remote access to the controller device, provide real-time information from the controller device, receive configurations/updates, provide interactive graphical user interfaces, and/or the like.

Controller devices may be configured to automatically connect to a remote management server (e.g., a “cloud”-based management server), and may offload received data and analyses to the remote management server via wired or wireless communications. The controller devices may further communicate with the management server, user computing devices, and/or human machine interface devices, e.g., to provide remote access to the controller device, provide real-time information from the controller device, receive configurations/updates, provide interactive graphical user interfaces, and/or the like.

Disclosed in various examples are methods, systems, and machine-readable media for exposing a Representational State Transfer (RESTful) interface to users whereby management commands on a datacenter may be issued remotely from the users' workstations for secure, remote management of the datacenter. An application task automation command (e.g., a POWERSHELL® command) is executed remotely by creating a proxy command (e.g., based on a POWERSHELL® cmdlet code) to cause the application task automation command to be executed when the proxy command is remotely invoked and deploying the proxy command to a remote computer, such as the user's workstation. The remote computer issues a request including a user identifier and any parameters for the application task automation command when the corresponding proxy command has been invoked by the remote computer. The datacenter determines whether the user is authorized to execute the application task automation command invoked by the proxy command, and upon authorization of the user, the datacenter computer runs the application task automation command with any parameters provided in the request to control configuration of, or data stored on, at least one computer in the datacenter.

Hybrid edge computing that includes a nimble framework that identifies services for available in a marketplace. The nimble framework defines a location for computing the services selected from the group consisting of a center server, an edge provision server and an edge node. The hybrid edge computing further includes a third party provider making are request for a service to the nimble framework. The hybrid edge computing further includes a virtualized service being provided by the nimble framework to the third party provider including a matched service to the third party provider request for the service, and an optimal location for computing.

Hybrid edge computing that includes a nimble framework that identifies services for available in a marketplace. The nimble framework defines a location for computing the services selected from the group consisting of a center server, an edge provision server and an edge node. The hybrid edge computing further includes a third party provider making are request for a service to the nimble framework. The hybrid edge computing further includes a virtualized service being provided by the nimble framework to the third party provider including a matched service to the third party provider request for the service, and an optimal location for computing.