Abstract: Unauthenticated client access to an application (e.g., a SaaS-based web application) that employs unauthenticated API endpoints is monitored and protected by an access control system and method that leverages a neural network. The neural network is trained to recognize user behaviors that should be deemed to be “inappropriate” according to a policy. Using the neural network, the system provides effective discrimination with respect to unauthenticated user behavior, and it enables access controls to be more effectively enforced with respect to users that are not using the application according to an enterprise security policy. By training the neural network to recognize pattern(s) behind regular user behavior, the approach enables robust access control with respect to users that are unauthenticated.

Abstract: A network-accessible cyber-threat security analytics service is configured to receive and respond to requests that originate as name queries to a Domain Name System (DNS) service. Threat intelligence information provided by the service is organized into threat intelligence zones that correspond to zones exposed via the DNS service. Upon receipt of a DNS query, the query having been generated by an application seeking access to threat intelligence data exposed by the service, the query is translated into a DNS zone-specific API request based on the type of threat intelligence information sought. The zone-specific API request is then used to retrieve the requested threat intelligence information from a threat intelligence database. The requested threat intelligence information is then returned to the application by being encoded as part of a response to the DNS query. In this manner, the DNS protocol is leverage to facilitate highly-efficient access and retrieval of threat intelligence information.

Abstract: A network-accessible cyber-threat security analytics service is configured to receive and respond to requests that originate as name queries to a Domain Name System (DNS) service. Threat intelligence information provided by the service is organized into threat intelligence zones that correspond to zones exposed via the DNS service. Upon receipt of a DNS query, the query having been generated by an application seeking access to threat intelligence data exposed by the service, the query is translated into a DNS zone-specific API request based on the type of threat intelligence information sought. The zone-specific API request is then used to retrieve the requested threat intelligence information from a threat intelligence database. The requested threat intelligence information is then returned to the application by being encoded as part of a response to the DNS query. In this manner, the DNS protocol is leverage to facilitate highly-efficient access and retrieval of threat intelligence information.

Abstract: Unauthenticated client access to an application (e.g., a SaaS-based web application) that employs unauthenticated API endpoints is monitored and protected by an access control system and method that leverages a neural network. The neural network is trained to recognize user behaviors that should be deemed to be “inappropriate” according to a policy. Using the neural network, the system provides effective discrimination with respect to unauthenticated user behavior, and it enables access controls to be more effectively enforced with respect to users that are not using the application according to an enterprise security policy. By training the neural network to recognize pattern(s) behind regular user behavior, the approach enables robust access control with respect to users that are unauthenticated.