A new audio quality assessment system includes an assessment system running in a receiver system of a VoIP communication system. The new audio quality assessment system determines an accurate MOS of a VoIP call within a time window. The audio quality assessment system determines an effective PLC counter, a PLC impact factor, an effective AS counter, an AS impact factor, a network impact factor, a codec type of the received voice packets, a bitrate of the received voice packets, an initial MOS from a configured codec-bitrate MOS table, and determines the accurate MOS based on these data. The determined MOS is more accurate and efficiently obtained since it is based on efficiently collected statistics of the receiver system's modules and a pre-configured codec-bitrate MOS table.

One example method of operation may include identifying a call from a calling device destined for a mobile device, responsive to identifying a calling device number assigned to the calling device, accessing a call content application programming interface (API), operated by a content delivery device, configured to deliver to the mobile device a first caller identification name (CNAM) or a second CNAM, assigned to the calling device number, determining a context assigned to a mobile device number assigned to the mobile device, selecting one of the first CNAM and the second CNAM based on the context, and assigning the selected CNAM to the call.

One example method of operation may include identifying a call from a calling device destined for a mobile device, identifying a calling device number associated with the calling device and a mobile device number associated with the mobile device, determining whether a stored calling relationship exists between the calling device number and the mobile device number, and appending one of a plurality of caller identification names (CNAM) to the call based on the determination as to whether there is a stored calling relationship.

Herein are machine learning (ML) techniques for unsupervised training with a corpus of signaling system 7 (SS7) messages having a diversity of called and calling parties, operation codes (opcodes) and transaction types, numbering plans and nature of address indicators, and mobile country codes and network codes. In an embodiment, a computer stores SS7 messages that are not labeled as anomalous or non-anomalous. Each SS7 message contains an opcode and other fields. For each SS7 message, the opcode of the SS7 message is stored into a respective feature vector (FV) of many FVs that are based on respective unlabeled SS7 messages. The FVs contain many distinct opcodes. Based on the FVs that contain many distinct opcodes and that are based on respective unlabeled SS7 messages, an ML model such as a reconstructive model such as an autoencoder is unsupervised trained to detect an anomalous SS7 message.

One example method of operation may include identifying a call from a calling device destined for a mobile device, responsive to identifying a calling device number assigned to the calling device, accessing a call content application programming interface (API), operated by a content delivery device, configured to deliver to the mobile device a first caller identification name (CNAM) or a second CNAM, assigned to the calling device number, determining a context assigned to a mobile device number assigned to the mobile device, selecting one of the first CNAM and the second CNAM based on the context, and assigning the selected CNAM to the call.

One example method of operation may include identifying a call from a calling device destined for a mobile device, identifying a calling device number associated with the calling device and a mobile device number associated with the mobile device, determining whether a stored calling relationship exists between the calling device number and the mobile device number, and appending one of a plurality of caller identification names (CNAM) to the call based on the determination as to whether there is a stored calling relationship.

Systems, methods, and apparatus to monitor mobile Internet activity are disclosed. An example apparatus includes at least one memory, machine-readable instructions, programmable circuitry to execute the machine-readable instructions to at least assign a first port of a proxy server to a mobile device associated with a panelist, cause transmission of configuration data to the mobile device to instruct the mobile device to transmit future requests the first port of the proxy server, obtain a first request for media on the first port originating from the mobile device, and after a determination that the first request originated from an Internet Protocol (IP) address associated with an IP address range representative of devices on a cellular network, service the first request, generate a data association, request the media from an Internet media provider identified in the first request, and cause transmission of the media to the mobile device.

An IA server includes a plurality of execution units that execute respective virtual machines of a plurality of disaster recovery stations corresponding one-to-one to a plurality of active stations. The plurality of disaster recovery stations includes a virtualized call control server that performs call processing when an active station is not operating, and a control unit that controls an operation of the virtual machines. The control unit activates, at normal times, the virtual machines, and deactivates, when any active station of the plurality of active stations is not operating, a virtual machine of a disaster recovery station of the plurality of disaster recovery stations other than a virtual machine of a disaster recovery station of the plurality of disaster recovery stations corresponding to the active station not operating, and operates only the virtual machine of the disaster recovery station corresponding to the active station not operating.

Systems, methods, and apparatus to monitor mobile Internet activity are disclosed. An example apparatus includes at least one memory, and processor circuitry to execute instructions to at least assign (i) a first port of a proxy server to a first mobile device associated with a first panelist and (ii) a second port of the proxy server to a second mobile device associated with a second panelist, in response to receiving an un-authenticated request for media from at least one of the first, second, or third mobile devices, service the un-authenticated request in response to determining that the un-authenticated request is from an Internet Protocol (IP) address associated with an IP address range representative of devices on a cellular network, and, in response to servicing the un-authenticated request via the first port, store an association between the media and first panelist demographic information associated with the first mobile device.

Aspects of the invention determining a threat score of a call traversing a telecommunications network by leveraging the signaling used to originate, propagate and terminate the call. Outer-edge data utilized to originate the call may be analyzed against historical, or third party real-time data to determine the propensity of calls originating from those facilities to be categorized as a threat. Storing the outer edge data before the call is sent over the communications network permits such data to be preserved and not subjected to manipulations during traversal of the communications network. This allows identification of threat attempts based on the outer edge data from origination facilities, thereby allowing isolation of a compromised network facility that may or may not be known to be compromised by its respective network owner. Other aspects utilize inner edge data from an intermediate node of the communications network which may be analyzed against other inner edge data from other intermediate nodes and/or outer edge data.