Abstract: In one example in accordance with the present disclosure, a method for domain classification includes sorting a set of sample domains into leaves based on syntactical features of the domains. Each sample domain belongs to a family of domains. The method also includes identifying, for each leaf, a regular expression for each family with at least one domain in the leaf. The method also includes determining, for each leaf, at least one lobe with a set of domains in the leaf that matches the regular expression for a first family with at least one domain in the leaf, and that does not match the regular expression for the other families with at least one domain in the leaf. The method also includes creating a classifier for the domains in each lobe by using the set of domains from each family in the lobe as training classes for machine learning.

Abstract: Modular transfer units are configured to transport bins or other storage units of standard shapes and sizes between locations. The modular transfer units may be loaded with bins or other storage units having items disposed therein at origins of the items, or at locations where the items are stored, such as fulfillment centers. The modular transfer units may then be mounted to one or more vehicles, such as trailers or rail cars, and transported to another location, where the modular transfer units may be removed from the vehicle, or transferred to another vehicle. The bins or other storage units may be loaded with items that are intended for delivery to a common location. After a bin or storage unit has been unloaded from a modular transfer unit, one or more other bins or storage units may be loaded thereon.

Abstract: Examples relate to collaborative security lists. The examples disclosed herein enable obtaining a first candidate entry suggested by a first user of a community to be included in a collaborative security list. The collaborative security list may comprise a list of entries known to be secure or a list of entries known to be insecure. The examples disclosed herein further enable providing a candidate security list comprising at least the first candidate entry to the community and obtaining, from a second user of the community, a first score indicating how confident the second user is that the first candidate entry is secure. The examples disclosed herein further enable determining whether to include the first candidate entry in the collaborative security list based on the first score.

Abstract: Examples determine a number of hosts, within an enterprise, which are resolving a particular domain. Based on the number of hosts within the enterprise resolving the particular domain, the examples identify whether the particular domain is benign.

Abstract: Mobile modules including a number of bins or other storage units may be formed in a modular manner, from various sub-modules. The mobile modules may be loaded with items by a sortation system or by associates. A bin within a mobile module may be selected and assigned to an order for a plurality of items, and each of the items included in the order may be deposited into the selected bin. After a sufficient number of bins within the mobile module have been filled with ordered items, the mobile module may be loaded into or onto a delivery vehicle and transported to a location from which the ordered items may be delivered to a customer, e.g., by an autonomous mobile robot or by an associate.

Abstract: In one implementation, a resource classification system identifies a plurality of resource requests and generates a plurality of resource access measures based on the plurality of resource requests. Each resource request from the plurality of resource requests is associated with a resource from a plurality of resources by a resource identifier of that resource. Each resource access measure from the plurality of resource access measures is associated with a resource from the plurality of resources. The resource classification system applies a classifier to each resource access measure from the plurality of resource access measures to generate a classification result for the resource from the plurality of resources associated with that resource access measure, and assign a security classification to each resource from the plurality of resources based on the classification result for that resource.

Abstract: Examples determine a number of hosts, within an enterprise, which are resolving a particular domain. Based on the number of hosts within the enterprise resolving the particular domain, the examples identify whether the particular domain is benign.

Abstract: In one example in accordance with the present disclosure, a method for domain classification includes sorting a set of sample domains into leaves based on syntactical features of the domains. Each sample domain belongs to a family of domains. The method also includes identifying, for each leaf, a regular expression for each family with at least one domain in the leaf. The method also includes determining, for each leaf, at least one lobe with a set of domains in the leaf that matches the regular expression for a first family with at least one domain in the leaf, and that does not match the regular expression for the other families with at least one domain in the leaf. The method also includes creating a classifier for the domains in each lobe by using the set of domains from each family in the lobe as training classes for machine learning.

Abstract: Examples relate to collaborative security lists. The examples disclosed herein enable obtaining a first candidate entry suggested by a first user of a community to be included in a collaborative security list. The collaborative security list may comprise a list of entries known to be secure or a list of entries known to be insecure. The examples disclosed herein further enable providing a candidate security list comprising at least the first candidate entry to the community and obtaining, from a second user of the community, a first score indicating how confident the second user is that the first candidate entry is secure. The examples disclosed herein further enable determining whether to include the first candidate entry in the collaborative security list based on the first score.

Abstract: In one implementation, a physical version of a document is converted to a digital representation of the physical version and a new certificate is computed for the digital representation of the physical version. The new certificate is computed based on the digital representation of the physical version, at least one processing step performed on the digital representation, and a prior certificate computed for a prior digital representation indicative of the physical version. In another implementation, a document is validated.

Abstract: Examples herein disclose packet size information collected over an encrypted tunnel. The examples identify an application communicated via the encrypted tunnel based on the packet size information.

Abstract: A method for submatch extraction may include receiving an input string, receiving a regular expression. The method may further include converting the regular expression with capturing groups into ordered binary decision diagrams (OBDDs) to extract submatches.

Abstract: A method for authenticating a user's location in a femtocell-based network is disclosed. A user is associated to a femtocell connected to a connection point in the wireless network. An indication of the user's location is provided to a remote user. A characteristic of a connection between the femtocell and the connection point is monitored. The monitored characteristic is provided to the remote user to verify user's location.

Abstract: In one implementation, a resource classification system identifies a plurality of resource requests and generates a plurality of resource access measures based on the plurality of resource requests. Each resource request from the plurality of resource requests is associated with a resource from a plurality of resources by a resource identifier of that resource. Each resource access measure from the plurality of resource access measures is associated with a resource from the plurality of resources. The resource classification system applies a classifier to each resource access measure from the plurality of resource access measures to generate a classification result for the resource from the plurality of resources associated with that resource access measure, and assign a security classification to each resource from the plurality of resources based on the classification result for that resource.

Abstract: A method for submatch extraction may include receiving an input string, receiving a regular expression, and converting the regular expression with capturing groups into a plurality of finite automata to extract submatches. The method further includes using a first automaton to determine whether the input string is in a language described by the regular expression, and to process the input string, and using states of the first automaton in a second automaton to extract the submatches.

Abstract: Instances of router models and filter models respectively are populated with configuration data from routers and filters in a network. A route advertising graph is derived from the router model instances. The route advertising graph indicates propagation of routes between the ones of the real-world devices serving as routers according to routing protocols implemented by the ones of the real-world devices serving as routers. Consolidated routing data is determined for the ones of the real-world devices serving as routers. In this process, the propagation of routes indicated by the route advertising graph is iterated to stability. For a destination node in the network, a respective route graph indicating available paths to the destination node from each source node in the network is constructed from the consolidated routing data. Services between each source node and the destination node are classified based on a full traversal of the route advertising graph.

Abstract: In one implementation, a resource reference classification system includes a selection engine and a classification engine. The selection engine is to access a plurality of resource request records based on resource requests intercepted from a plurality of clients, and to select resource request records from the plurality of resource request records intercepted from a client from the plurality of clients. Each resource request record from the plurality of resource request records includes a resource reference. The classification engine is to identify, independent of the client, a root resource reference and a plurality of child resource references of the root resource reference from the resource request records.

Abstract: Sharing information can include identifying, utilizing a threat exchange server, a security occurrence associated with a participant within a threat exchange community. Sharing information can also include determining what participant-related information to share with the threat exchange server in response to the identified security occurrence, and receiving, at the threat exchange server, information associated with the determined participant-related information via communication links within the threat exchange community.

Abstract: Threat exchange information protection can include receiving security information from a number of participants of a threat exchange community, wherein a portion of the received security information is encoded with pseudonyms by each of the number of participants, analyzing the security information collectively from the number of participants, wherein the portion of the received security information remains encoded, and sending analysis results to each of the number of participants, wherein the analysis results include information relating to the portion.

Abstract: In one implementation, a security alert prioritization system identifies a host and a domain associated with a security alert that was generated in response to a communication between the host and the domain. The security alert prioritization system accesses a security state associated with the host and a security state associated with the domain, and compute a priority of the security alert based on the security state associated with the host and the security state associated with the domain.