Assignment of resource criticality scores to cloud resources based on cloud resource class

A method of operating a training component, comprising:

• receiving information samples that characterize attributes of a set of cloud resources of a cloud network,
• wherein the cloud network comprises a frontend platform and a backend platform,
• wherein the frontend platform comprises frontend client infrastructure for interfacing with clients,
• wherein the backend platform comprises backend platform infrastructure that comprises a group of distributed and interconnected computing devices with shareable hardware and/or software resources that support distributed implementation of a set of cloud applications via a respective set of cloud resources,
• wherein each cloud resource of the set of cloud resources is associated with a set of shareable hardware and/or software resources of the backend platform, and
• wherein each cloud resource of the set of cloud resources is associated with a cloud resource class that corresponds to one of a plurality of cloud resource classes supported by the cloud network;
• assigning resource criticality scores for each cloud resource class associated with the set of cloud resource classes based on the information samples and rule-based heuristics;
• and
• training, for each cloud resource class associated with the set of cloud resource classes, a machine-learning (ML)-based classifier or regressor associated with the respective cloud resource class by inputting the information samples and the resource criticality scores for the respective cloud resource class as training data.

A security component, comprising:

• one or more memories;
• one or more transceivers;
• and
• one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to:
• receive, via the one or more transceivers, one or more information samples that characterize one or more attributes of a cloud resource of a cloud network,
• wherein the cloud network comprises a frontend platform and a backend platform,
• wherein the frontend platform comprises frontend client infrastructure for interfacing with clients,
• wherein the backend platform comprises backend platform infrastructure that comprises a group of distributed and interconnected computing devices with shareable hardware and/or software resources that support distributed implementation of a set of cloud applications via a respective set of cloud resources,
• wherein the cloud resource is associated with a set of shareable hardware and/or software resources of the backend platform, and
• wherein the cloud resource is associated with a cloud resource class that corresponds to one of a plurality of cloud resource classes supported by the cloud network;
• convert the one or more information samples into vectorized data associated with a set of attribute categories;
• derive a resource criticality score associated with the cloud resource based on the vectorized data and a machine-learning (ML)-based classifier or regressor that is trained based on training data associated with the cloud resource class that is processed by rule-based heuristics;
• and
• perform one or more infrastructure security functions based on the resource criticality score associated with the cloud resource.

A training component, comprising:

• one or more memories;
• one or more transceivers;
• and
• one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to:
• receive, via the one or more transceivers, information samples that characterize attributes of a set of cloud resources of a cloud network,
• wherein the cloud network comprises a frontend platform and a backend platform,
• wherein the frontend platform comprises frontend client infrastructure for interfacing with clients,
• wherein the backend platform comprises backend platform infrastructure that comprises a group of distributed and interconnected computing devices with shareable hardware and/or software resources that support distributed implementation of a set of cloud applications via a respective set of cloud resources,
• wherein each cloud resource of the set of cloud resources is associated with a set of shareable hardware and/or software resources of the backend platform, and
• wherein each cloud resource of the set of cloud resources is associated with a cloud resource class that corresponds to one of a plurality of cloud resource classes supported by the cloud network;
• assign resource criticality scores for each cloud resource class associated with the set of cloud resource classes based on the information samples and rule-based heuristics;
• and
• train, for each cloud resource class associated with the set of cloud resource classes, a machine-learning (ML)-based classifier or regressor associated with the respective cloud resource class by inputting the information samples and the resource criticality scores for the respective cloud resource class as training data.