Abstract: In various examples, firewalls may include machine learning models that are automatically trained and applied to analyze service inputs submitted to input processing services and to identify whether service inputs are desirable (e.g., will result in an undesirable status code if processed by a service). When a service input is determined by a firewall to be desirable, the firewall may push the service input through to the input processing service for normal processing. When a service input is determined by the firewall to be undesirable, the firewall may block or drop the service input before it reaches the input processing service and/or server. This may be used to prevent the service input, which is likely to be undesirable, from touching a server that hosts the input processing service (e.g., preventing a crash).

Abstract: In various examples, firewalls may include machine learning models that are automatically trained and applied to analyze service inputs submitted to input processing services and to identify whether service inputs are desirable (e.g., will result in an undesirable status code if processed by a service). When a service input is determined by a firewall to be desirable, the firewall may push the service input through to the input processing service for normal processing. When a service input is determined by the firewall to be undesirable, the firewall may block or drop the service input before it reaches the input processing service and/or server. This may be used to prevent the service input, which is likely to be undesirable, from touching a server that hosts the input processing service (e.g., preventing a crash).