In a first device in response to an event input, using a processor and a memory, a local classification and a local classification confidence score corresponding to the event input are computed. At the first device in response to a broadcast request, a remote classification and a remote classification confidence score corresponding to the event input are received, the remote classification and the remote classification confidence score being computed at a second device. At the first device, a consensus classification including the most frequent classification from a set of all received remote classifications and the local classification is formed, provided the number of classifications including the most frequent classification exceeds a threshold. In response to a consensus classification confidence score corresponding to the consensus classification exceeding a confidence threshold, a local classification model is updated. Based on the local classification and the consensus classification, the event input is assigned to a classification.

This invention discloses a wireless access node detecting method, a wireless network detecting system and a server. The server comprises: a receiving unit configured to receive a wireless network signal sent by a wireless network sensor, of which the wireless network signal is a wireless network signal received by the wireless network sensor within a coverage area; a parsing unit configured to parse out wireless network connection information comprising wireless access node information from the wireless network signal; and an analyzing unit configured to analyze the wireless access node information in the wireless network connection information, and generate an analysis result. By receiving wireless access node information within a monitoring area sent by multiple wireless network sensors disposed therein, analyzing it and conducting access control, the server can effectively ensure the security of a wireless network within an enterprise.

Various implementations described herein are directed to determining a multi-factor trust score. Data from one or more endpoint devices is received over a gateway. A trust score is determined based on a plurality of metrics. The plurality of metrics including at least: a first attestation process performed for the one or more endpoint devices, and a second attestation process performed for the gateway. The trust score is sent to an analytics server. The trust score is configured to be used by the analytics server to determine an alert and/or a command based on the trust score.

An analysis of the delivery characteristics of an automated vehicle and a comparison of received security credentials of the vehicle to acceptable credentials are performed. Based upon the results of the analysis and the comparison, a confidence score is determined. The confidence score is a numerical measure of trust in an authentic identity of the automated vehicle. The determined confidence score is compared to the confidence threshold and when the score exceeds the confidence threshold, a message is transmitted to the automated vehicle allowing the vehicle to deliver a package to a delivery location.

A multi-modal portal system provides an authorization prior to establishing a visual interface connection between a support user or caller and a client user or receiving party. Once the caller is authorized, the caller is permitted to drop-in such that a visual interface connection is established with the receiving party. The authorization can be based on a profile configuration that indicates whether the caller has the credentials required for the visual interface. The authorization can require that a notification be sent to a trusted user or primary contact or the caller can be associated with a profile configuration that allows for a pre-authorization without requiring the notification. Also, a trusted user can be provided access to control an optical instrument associated with a client user where the client user is associated profile configuration or both.

Embodiments of the present disclosure are directed to systems and methods for improving wireless network services by carrying out various procedures to identify and filter suspect user devices. A network function may monitor a plurality of network service requests from a particular user device and determine, based on the plurality of network services requests, that the requesting user device is engaged in suspicious activity. Upon such a determination, the network function may initiate one or more enforcement actions by communicating an instruction to an equipment identity register to add the requesting user device to a suspect device list stored on a unified data repository.

Apparatuses, methods, and systems are disclosed for notification of a new management domain feature. One method includes determining that a new management domain feature is available. The method includes determining at least one entity to be notified about the new management domain feature. The method includes determining an advertisement to be sent to the at least one entity to notify the at least one entity about the new management domain feature. The method includes transmitting the advertisement to the at least one entity.

An RRC connection maintenance method, a related device, and a readable storage medium, and pertains to the field of communications technologies. The RRC connection maintenance method includes: establishing, by a primary terminal, a first RRC connection to a network side device, where the primary terminal is a terminal among Q terminals, the primary terminal supports transmitting information about some or all of the Q terminals, and the Q terminals support receiving data of a first service together, where Q is an integer greater than 1.

A method for determining, onboard a first mobile entity, a level of trust for a second mobile entity in communication with the first mobile entity, and a communication system implementing the method are described. The method includes determining), by the first mobile entity, direct trust data, calculating an initial reputation indicator based on the direct trust data, including a belief value, a disbelief value, an uncertainty value, and a base trust rate, calculating a confidence level based on the belief value, the base trust rate, and the uncertainty value, and determining an updated reputation indicator based on the initial reputation indicator and the confidence level. The base trust rate is indicative of the trust level for data transmitted from an unknown mobile entity not in contact with the first mobile entity. The updated reputation indicator indicates a level of trust of the first mobile entity in the second mobile entity.

Systems, apparatus, methods, and techniques for an ego vehicle to respond to detecting misbehaving information from remote vehicles are provided. An ego vehicle, in addition to reporting misbehaving vehicles to a misbehavior authority via a vehicle-to-anything communication network, can, take additional actions based in part on how confident the ego vehicle is about the evidence of misbehavior. Where the confidence is high the ego vehicle can simply discard the misbehaving data and provide an alternative estimate for such data from alternative sources. Where the confidence is not high the ego vehicle can request assistance from neighboring vehicles and roadside units to provide independent estimates of the data to increase confidence in the evidence of misbehavior.