Verizon Patent and Licensing Inc.

Santanu Das

Ashok N Srivastava

Hai Shao

Kalyan Pamarthy

Abhijit S Kudrimoti

Projit Aon

Yunzhu Chen

An Le

Xuepeng Sun

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 device receives information associated with machine-to-machine (M2M) devices connected to a host server device via a network. The information associated with the M2M devices include one or more of device information associated with components of the M2M devices, application information generated by the M2M devices, or network information associated with interactions of the M2M devices, with the network, when the M2M devices provide the application information to the host server device via the network. The device performs an analysis of the information associated with the M2M devices via one or more analytics techniques, and generates analysis information based on the analysis of the information associated with the M2M devices. The device provides the analysis information for display by the host server device.

Scalable framework for monitoring machine-to-machine (M2M) devices

A method includes receiving, at a fraud management device, at least one BSID associated with communication data from at least one MTC device. The fraud management device determines a location associated with the at least one BSID. Relevant data elements are selected from the communication data. The fraud management device applies known domain rules to the communication data and identifies a connectivity pattern for the at least one MTC device based on the communication data. An anomaly detection model is applied to particular communication data associated with a particular MTC device based on the connectivity pattern, and at least one anomaly from the identified connection pattern is detected based on the anomaly detection model.

Location fraud detection

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Santanu Das