Verizon Patent and Licensing Inc.

Hai Shao

Ashok N Srivastava

Deepak Kakadia

Santanu Das

Stephen P Boyd

Michael Rinehart

Li Yang

Yong Gao

Zhengming Xing

A device may receive behavior information that identifies a first user, of a first set of users, in association with a behavior. The behavior may relate to one or more requests, from a client device being used by the first user, to access a network resource. The device may determine, based on a model, whether the behavior is normal. The model may include a normal behavior pattern based on behavior information associated with the first set of users. The device may provide an instruction to allow the client device to proceed with the behavior or provide an instruction to disallow the client device from proceeding with the behavior based on determining whether the behavior is normal. The device may update the model based on the behavior information that identifies the first user and that identifies the behavior.

Network threat detection and management system based on user behavior information

A method and device may estimate the accuracy of position data using kernel density estimator. The method may include receiving, from a plurality of user devices, network requests having embedded position data representing locations of the plurality of user devices. The method further includes extracting, from the network requests over a time period, the embedded position data of a user device associated with the plurality of user devices; and receiving baseline position data representing the locations of the user device over the time period. The method included generating a probability density estimate of the locations of the user device based on a kernel density estimator using the baseline position data, determining accuracy scores for the embedded position data using the probability density estimate of the locations, and filtering the embedded position data to remove outliers from the embedded position data.

Accuracy estimation and enhancement of position data using kernel density estimation

A device may receive performance information for a traffic flow assigned to a quality of service (QoS) class. The device may determine an overall packet delay, associated with the traffic flow, based on the performance information. The device may determine a radio access network (RAN) delay, associated with the traffic flow, based on the performance information. The device may determine a target packet delay associated with the QoS class. The device may identify, based on the target packet delay, the RAN delay, and the overall packet delay, a QoS sub-class to which the traffic flow is to be assigned. The QoS sub-class may be associated with the QoS class. The device may cause packets, associated with the traffic flow, to be marked for treatment in accordance with the QoS sub-class.

Optimized quality of service transport architecture to compensate for SINR variations

A device may receive traffic information associated with traffic flows assigned to a group of quality of service (QoS) treatment levels and travelling via a network device. The device may identify a group of target performance metrics corresponding to the group of QoS treatment levels. The device may determine a group of weight factors based on the traffic information and the group of target performance metrics. The group of weight factors may be determined such that a group of predicted performance metrics is optimized with respect to the group of target performance metrics. The device may output information identifying the group of weight factors to cause a parameter, associated with the network device, to be updated based on the group of weight factors. The parameter may relate to a manner in which the traffic flows are processed by the network device.

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Fremont, CA, United States of America

Hai Shao