Abstract: The present disclosure is related to a computer-implemented method and system for distinguishing human-driven Doman Name System (DNS) queries from Machine-to-Machine (M2M) DNS queries. The method includes receiving a DNS query, which includes a domain name, generating a probability score for the domain name based on one or more predetermined rules, and categorizing the DNS query as a human-driven DNS query or a M2M DNS query based on the probability score.

Abstract: The present disclosure is related to a computer-implemented method and system for distinguishing human-driven Doman Name System (DNS) queries from Machine-to-Machine (M2M) DNS queries. The method includes receiving a DNS query, which includes a domain name, generating a probability score for the domain name based on one or more predetermined rules, and categorizing the DNS query as a human-driven DNS query or a M2M DNS query based on the probability score.

Abstract: The present disclosure is related to a computer-implemented method and system for distinguishing human-driven Domain Name System (DNS) queries from Machine-to-Machine (M2M) DNS queries. The method includes receiving a DNS query, which includes a domain name, generating a probability score for the domain name based on one or more predetermined rules, and categorizing the DNS query as a human-driven DNS query or a M2M DNS query based on the probability score.

Abstract: A method for supplementing a content policy is provided. The method may include receiving a request to access network content associated with a network content, with the network content including additional network content associated with one or more linked network contents. The method may confirm that the network content is associated with the content policy. The content policy may include a list of pre-approved network contents. Based on the confirmation, the method may selectively provide access to the network content and apply the content policy to the additional network content associated with the one or more linked network contents. A policy enforcement module may determine whether or not the additional network content should be included in the network content, and, based on the determination, selectively allow inclusion of the additional network content within the network content.

Abstract: The present disclosure is related to a computer-implemented method and system for distinguishing human-driven Domain Name System (DNS) queries from Machine-to-Machine (M2M) DNS queries. The method includes receiving a DNS query, which includes a domain name, generating a probability score for the domain name based on one or more predetermined rules, and categorizing the DNS query as a human-driven DNS query or a M2M DNS query based on the probability score.

Abstract: Provided are computer-implemented methods and systems for analyzing domain name system requests and developing profiles associated with these requests. Multiple requests received from the same internet protocol (IP) address may be analyzed to differentiate computer systems used to generate these requests, applications provided on these computer systems, and even different users. The requests are analyzed based on text string content (e.g., domain and subdomain names) and timing. One or more profiles are developed and continuously updated based on requests received from the same IP address. These profiles may be used in real time to provide feedback to the users (e.g., deliver marketing content) or for subsequent analysis of comprehensive data sets (e.g., to identify behavior patterns). For example, a profile may be used to identify a number and types of computers in the household, a number and demographic information of users, and other such identifiers.