Abstract: In one embodiment, a service computes a plurality of features of a subdomain for which a Domain Name System (DNS) query was issued. The service aggregates the plurality of computed features into a feature vector. The service uses the feature vector as input to a machine learning classifier, to determine whether the subdomain is a DNS tunneling domain name. The service provides an indication that the subdomain is a DNS tunneling domain name, when the machine learning classifier determines that the subdomain is a DNS tunneling domain name.

Abstract: In one embodiment, a device obtains certificate information for a plurality of network addresses. The device constructs, based on the certificate information, a bipartite graph that maps nodes representing common names from the certificate information to nodes representing autonomous systems. The device determines edge counts from the bipartite graph for the nodes representing the autonomous systems. The device identifies, based on the edge counts, a particular one of the common names as botnet-related by comparing edge counts for the autonomous systems associated with that particular common name to edge counts for the autonomous systems associated with one or more of the other common names.

Abstract: In one embodiment, a service computes a plurality of features of a subdomain for which a Domain Name System (DNS) query was issued. The service aggregates the plurality of computed features into a feature vector. The service uses the feature vector as input to a machine learning classifier, to determine whether the subdomain is a DNS tunneling domain name. The service provides an indication that the subdomain is a DNS tunneling domain name, when the machine learning classifier determines that the subdomain is a DNS tunneling domain name.

Abstract: Techniques are provided herein for classifying domains based on DNS traffic so that domains that are malicious or associated with malicious activity can be identified. Traffic between one or more domain name system (DNS) resolvers and one or more authoritative name servers hosted on the Internet is analyzed analyzing at a server having network connectivity. A mismatch between a hostname and Internet Protocol (IP) information for the hostname is detected in the traffic and domains included in the traffic are classified based on the detecting.

Abstract: A method and an apparatus for detecting botnet domains is described. In one embodiment, the method includes monitoring network traffic associated with a plurality of clients in a network. Based on the monitoring, information related to a plurality of domains that are queried is stored. The method includes identifying one or more suspect clients in the network based on the stored information and determining a subset of suspect domains based on the stored information related to the domains queried by the suspect clients. The method can include determining client activity information and using the client activity information to determine a polytope region for a client. The method includes comparing each suspect domain to the polytope region and associating a domain with a group of blocked domains if the domain falls within the polytope region.

Abstract: In one embodiment, a device obtains certificate information for a plurality of network addresses. The device constructs, based on the certificate information, a bipartite graph that maps nodes representing common names from the certificate information to nodes representing autonomous systems. The device determines edge counts from the bipartite graph for the nodes representing the autonomous systems. The device identifies, based on the edge counts, a particular one of the common names as botnet-related by comparing edge counts for the autonomous systems associated with that particular common name to edge counts for the autonomous systems associated with one or more of the other common names.

Abstract: Techniques are provided herein for classifying domains based on DNS traffic so that domains that are malicious or associated with malicious activity can be identified. Traffic between one or more domain name system (DNS) resolvers and one or more authoritative name servers hosted on the Internet is analyzed analyzing at a server having network connectivity. A mismatch between a hostname and Internet Protocol (IP) information for the hostname is detected in the traffic and domains included in the traffic are classified based on the detecting.

Abstract: Techniques are provided herein for classifying domains based on DNS traffic so that domains that are malicious or associated with malicious activity can be identified. Traffic between one or more domain name system (DNS) resolvers and one or more authoritative name servers hosted on the Internet is analyzed analyzing at a server having network connectivity. A mismatch between a hostname and Internet Protocol (IP) information for the hostname is detected in the traffic and domains included in the traffic are classified based on the detecting.

Abstract: Techniques are provided herein for classifying domains based on DNS traffic so that domains that are malicious or associated with malicious activity can be identified. Traffic between one or more domain name system (DNS) resolvers and one or more authoritative name servers hosted on the Internet is analyzed analyzing at a server having network connectivity. A mismatch between a hostname and Internet Protocol (IP) information for the hostname is detected in the traffic and domains included in the traffic are classified based on the detecting.

Abstract: Techniques are provided herein for classifying domains based on DNS traffic so that domains that are malicious or associated with malicious activity can be identified. Malicious domains are identified by analyzing, at a server having network connectivity, traffic between one or more clients and one or more Domain Name System (DNS) resolvers, detecting a spike in the traffic for a particular domain, and categorizing queries in the spike based on one or more query features. The particular domain is classified based on the categorizing.