Abstract: A system and method for cloud native virtual machine (VM) runtime protection. The method includes creating a normal behavior model for a cloud native VM by training a machine learning model using a training data set including training activities performed by the cloud native VM, the cloud native VM being configured to provide at least one service, wherein the normal behavior model defines at least one capability of each service based on a set of capabilities for respective known services stored within a library of service-to-capability mappings, wherein each capability of a service indicates a plurality of discrete behaviors required by the service; and monitoring an execution of the cloud native VM to detect a deviation from the normal behavior model, wherein the deviation is caused by at least one abnormal behavior of one of the services that is not among the discrete behaviors defined in capabilities for the service.

Abstract: A system and method for cloud native virtual machine (VM) runtime protection. The method includes creating a normal behavior model for a cloud native VM by training a machine learning model using a training data set including training activities performed by the cloud native VM, the cloud native VM being configured to provide at least one service, wherein the normal behavior model defines at least one capability of each service based on a set of capabilities for respective known services stored within a library of service-to-capability mappings, wherein each capability of a service indicates a plurality of discrete behaviors required by the service; and monitoring an execution of the cloud native VM to detect a deviation from the normal behavior model, wherein the deviation is caused by at least one abnormal behavior of one of the services that is not among the discrete behaviors defined in capabilities for the service.

Abstract: A system and method for cloud native virtual machine (VM) runtime protection. The method includes creating a normal behavior model for a cloud native VM by training a machine learning model using a training data set including training activities performed by the cloud native VM, the cloud native VM being configured to provide at least one service, wherein the normal behavior model defines at least one capability of each service based on a set of capabilities for respective known services stored within a library of service-to-capability mappings, wherein each capability of a service indicates a plurality of discrete behaviors required by the service; and monitoring an execution of the cloud native VM to detect a deviation from the normal behavior model, wherein the deviation is caused by at least one abnormal behavior of one of the services that is not among the discrete behaviors defined in capabilities for the service.

Abstract: A system and method for cloud native virtual machine (VM) runtime protection. The method includes creating a normal behavior model for a cloud native VM by training a machine learning model using a training data set including a plurality of training activities performed by the cloud native VM, the cloud native VM being configured to provide at least one service, wherein the normal behavior model defines at least one capability of each of the at least one service, wherein each capability of a service indicates a plurality of discrete behaviors required by the service; and monitoring an execution of the cloud native VM to detect a deviation from the normal behavior model, wherein the deviation is caused by at least one abnormal behavior of one of the at least one service that is not among the discrete behaviors defined in the at least one capability for the service.

Abstract: A system and method for cloud native virtual machine (VM) runtime protection. The method includes creating a normal behavior model for a cloud native VM by training a machine learning model using a training data set including training activities performed by the cloud native VM, the cloud native VM being configured to provide at least one service, wherein the normal behavior model defines at least one capability of each service based on a set of capabilities for respective known services stored within a library of service-to-capability mappings, wherein each capability of a service indicates a plurality of discrete behaviors required by the service; and monitoring an execution of the cloud native VM to detect a deviation from the normal behavior model, wherein the deviation is caused by at least one abnormal behavior of one of the services that is not among the discrete behaviors defined in capabilities for the service.

Abstract: A system and method for serverless runtime application self-protection.

Abstract: A system and method for serverless runtime application self-protection.

Abstract: A system and method for cloud native virtual machine (VM) runtime protection. The method includes creating a normal behavior model for a cloud native VM by training a machine learning model using a training data set including a plurality of training activities performed by the cloud native VM, the cloud native VM being configured to provide at least one service, wherein the normal behavior model defines at least one capability of each of the at least one service, wherein each capability of a service indicates a plurality of discrete behaviors required by the service; and monitoring an execution of the cloud native VM to detect a deviation from the normal behavior model, wherein the deviation is caused by at least one abnormal behavior of one of the at least one service that is not among the discrete behaviors defined in the at least one capability for the service.