Disclosed herein are systems and methods for determining learned associations between authentication credentials and network contextual data, such as may be utilized in a network that supports network roving. A mobile device attempts to rove to a visited network using authentication credentials associated with another network, based at least in part on first contextual information associated with the other network and second contextual information associated with the visited network indicating that the visited network is part of a common association of networks that supports roving internetworking between the networks of the common association.

A network communications method utilizing a network watermark for providing security in the communications includes creating a verifiable network communications path of nodes through a network for the transfer of information from a first end node to a second end node; verifying the network communications path of nodes, by the first end node, before communicating by the first end node information intended for receipt by the second end node; and once the network communications path of nodes is verified by the first end node, communicating by the first end node, via the verified communications path of nodes, the information intended for receipt by the second end node; wherein the network watermark represents the verifiable network communications path of nodes.

Systems and methods for token processing are disclosed. An access device can provide access device data to a mobile communication device. The communication device generates a token request including the access device data and communication device data and sends the token request to a server computer. The server computer returns a token and a token cryptogram to the mobile communication device. The token and the cryptogram may be used in a transaction.

The present disclosure relates to a method for displaying a WiFi signal icon on a mobile terminal. The method includes determining a security level of a current WiFi network, marking a WiFi signal icon according to a color corresponding to the security level, and displaying the marked WiFi signal icon on the mobile terminal.

A method for monitoring an unmanned aerial vehicle (UAV) includes a processor obtaining a datagram based on monitoring data for a UAV-detector communication between the UAV and one or more detectors. The monitoring data indicates at least one of a location of the UAV or a location of a control station in communication with the UAV. The method further includes determining a risk level by retrieving pre-stored risk information associated with the UAV based on the datagram.

Preserving user privacy and preventing surveillance on behalf of users of a virtual reality world. One or more plans are available when a privacy or surveillance risk to a user is detected. In one plan, configurable scripts execute on behalf of the user to create a confusing array of clone avatars that obfuscate the real user avatar behavior. A malevolent avatar, attempting to surveil the user, may have difficulty distinguishing the clones from the user and may miss out on private insights he might otherwise have learned from the user's behavior. In another exemplary privacy plan, a copy of part of the virtual world is spawned, occupied exclusively by the user's avatar, and then merged into the main world. Privacy plans may be selected manually or automatically in response to perceived privacy threats to strike a balance between privacy and enjoyment within the virtual world.

Preserving user privacy and preventing surveillance on behalf of users of a virtual reality world. One or more plans are available when a privacy or surveillance risk to a user is detected. In one plan, configurable scripts execute on behalf of the user to create a confusing array of clone avatars that obfuscate the real user avatar behavior. A malevolent avatar, attempting to surveil the user, may have difficulty distinguishing the clones from the user and may miss out on private insights he might otherwise have learned from the user's behavior. In another exemplary privacy plan, a copy of part of the virtual world is spawned, occupied exclusively by the user's avatar, and then merged into the main world. Privacy plans may be selected manually or automatically in response to perceived privacy threats to strike a balance between privacy and enjoyment within the virtual world.

Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect individual consumers, business enterprises, and other organizations from identity theft and fraud. Risks associated with various entities can be analyzed and assessed based on analysis of social network data, professional network data, or other networking connections, among other data sources. In various embodiments, the risk assessment may include calculating an authenticity score based on the collected network data.

Methods and apparatus automatically identify which Internet-of-Things (IoT) devices within a set are behaving in a manner non-compliant with a target behavior. Each IoT device is assigned to a grid point in a notional m-dimensional space. A respective assessment module is arranged to monitor behavior of a group of IoT devices assigned to grid points aligned with one another at a respective position along the respective dimension, and to produce an output indicative of non-compliant behavior if the monitoring indicates that behavior in the group of IoT devices deviates from a behavioral model of the IoT devices of said group. An identification module identifies at least one non-compliant IoT device in the set of IoT devices by combining outputs from the assessment modules assigned to the different dimensions of the space. The assessment modules may use trained machine-learning algorithms embodying a model of normal behavior of the group of devices.

Methods and apparatus automatically identify which Internet-of-Things (IoT) devices within a set are behaving in a manner non-compliant with a target behavior. Each IoT device is assigned to a grid point in a notional m-dimensional space. A respective assessment module is arranged to monitor behavior of a group of IoT devices assigned to grid points aligned with one another at a respective position along the respective dimension, and to produce an output indicative of non-compliant behavior if the monitoring indicates that behavior in the group of IoT devices deviates from a behavioral model of the IoT devices of said group. An identification module identifies at least one non-compliant IoT device in the set of IoT devices by combining outputs from the assessment modules assigned to the different dimensions of the space. The assessment modules may use trained machine-learning algorithms embodying a model of normal behavior of the group of devices.