Abstract: A device may receive log data identifying access of user devices to containers provided by a cloud-based network, and may aggregate the log data to generate aggregated log data identifying container profiles associated with the containers. The device may train one or more machine learning models, with the aggregated log data, to generate one or more trained machine learning models, and may receive particular log data identifying access of a particular user device, of the user devices, to a particular container of the containers provided by the cloud-based network. The device may process the particular log data, with the one or more trained machine learning models, to identify an anomaly associated with the access of the particular user device to the particular container, and may perform one or more actions based on identifying the anomaly.

Abstract: An orchestrator platform may receive information identifying container attributes from a plurality of containers. The orchestrator platform may create aggregated information regarding the container attributes. The orchestrator platform may analyze the aggregated information to assign containers to container clusters. The orchestrator platform may analyze script information to assign scripts to script clusters. The orchestrator platform may match the script clusters and the container clusters. The orchestrator platform may generate information regarding a set of scripts for a container cluster based on the matching. The orchestrator platform may send the information regarding the set of scripts to a container to cause the container to obtain one or more scripts and execute the one or more scripts. The orchestrator platform may receive execution results. The orchestrator platform may determine whether a set of rules are satisfied.

Abstract: A device may receive log data identifying access of user devices to containers provided by a cloud-based network, and may aggregate the log data to generate aggregated log data identifying container profiles associated with the containers. The device may train one or more machine learning models, with the aggregated log data, to generate one or more trained machine learning models, and may receive particular log data identifying access of a particular user device, of the user devices, to a particular container of the containers provided by the cloud-based network. The device may process the particular log data, with the one or more trained machine learning models, to identify an anomaly associated with the access of the particular user device to the particular container, and may perform one or more actions based on identifying the anomaly.

Abstract: An orchestrator platform may receive information identifying container attributes from a plurality of containers. The orchestrator platform may create aggregated information regarding the container attributes. The orchestrator platform may analyze the aggregated information to assign containers to container clusters. The orchestrator platform may analyze script information to assign scripts to script clusters. The orchestrator platform may match the script clusters and the container clusters. The orchestrator platform may generate information regarding a set of scripts for a container cluster based on the matching. The orchestrator platform may send the information regarding the set of scripts to a container to cause the container to obtain one or more scripts and execute the one or more scripts. The orchestrator platform may receive execution results. The orchestrator platform may determine whether a set of rules are satisfied.