Systems and methods for providing secure communication between an inmate and an outside user are disclosed. In various aspects, account information of both the user and the inmate is stored. A message transmission is received from a first device that includes a header portion and an encrypted payload portion. The system verifies that confidential communication between the inmate and the outside user is permitted based on the header portion and the stored account information. Once verified, the message is transmitted to a second device without decrypting the payload portion of the message.

Computer-implemented detection of anomalous telephone calls, for example detection of interconnect bypass fraud, is disclosed. A telephone call associated with user devices is analyzed remote from the user devices. A first set of multiple features, for example Mel Frequency Cepstral Coefficients, is derived from a call audio stream. The first set is converted to an embedding vector, for example via a model based on a Universal Background Model comprising a Gaussian Mixture Model, which model is preferably configured based on a training plurality of first sets of multiple features derived form a corresponding training plurality of audio streams. Occurrence, or probability of occurrence, of an anomalous telephone call is determined based on the embedding vector, for example via a back-end classifier, such as a Gaussian Backend Model, which classifier is preferably configured based on labels associated with the training plurality of audio streams.

Novel tools and techniques are provided for implementing call center-based automated interface between call center agents and network orchestration systems, in some cases, via application programming interfaces (“APIs”). In various embodiments, an automated services platform may collect, from data sources, data associated with services provided by a service provider. The collected data may be autonomously analyzed to identify any issues with provisioning a service(s) to a customer. If so, one or more automation actions may be autonomously identified to address the determined issue(s). The automated services platform may autonomously send instructions to automation bots to perform the identified one or more automation actions, and may generate and present a UI on a user terminal operated by a user, the UI displaying information regarding at least one of the at least one issue, the affected service(s), the identified one or more automation actions, or the automation bots, and/or the like.

A system and method of managing messages in a telecommunications network comprises: transmitting the first SMS message to a wireless device, the first SMS message including a first acknowledgement request and a content processing request; determining whether a first acknowledgement has been received from the wireless device in response to the first acknowledgement request within a first predetermined time period; in response to a determination that the first acknowledgement has not been received within the first predetermined time period: transmitting a second SMS message to the wireless device, the second SMS message including a second acknowledgement request and a content non-processing request; determining whether a second acknowledgement has been received from the wireless device in response to the second acknowledgement request; and in response to a determination that the second acknowledgement has been received within a second predetermined time period, retransmitting the first SMS message to the wireless device.

A device, method and system for changing communication infrastructure based on call security level is provided. A device determines a call security level of a call occurring at a first communication infrastructure; the first communication infrastructure associated with a first security level; the call security level determined from one or more of; a profile of a caller on the call; and audio on the call. In response to determining that the call security level and the first security level are misaligned, the device causes the call to change to a second communication infrastructure associated with a second security level aligned with the call security level.

Embodiments herein disclose methods and systems for detecting call spoofing in a telecommunication network. The system includes a memory , at least one processor , a call spoofing controller connected to the memory and the processor configured to: create a mirror of signaling protocols for an incoming call from first a user device . The call spoofing controller is further configured to determine if a caller phone number belongs to a network operator using a source Internet Protocol (IP) address in an incoming INVITE/Iniytial Address Message (IAM). The call spoofing controller is further configured to determine the incoming call as a spoofed call, if the source IP address does not match a Session Border Controller/Service Switching Point (SBC/SSP) of the network operator.

A mobile communications system includes switching between communication paths based on the quality of service. A VoIP service is coupled to a mobile network and a wireless local-area network (WLAN) via a wide-area IP network. A plurality of mobile communications devices are associated with VoIP telephone numbers. A public switched telephone network (PSTN) is coupled to the plurality of mobile communications devices and coupled to the VoIP service. A quality of service server is coupled to the plurality of mobile communications devices via the wide-area IP network for determining the quality of data channels to a mobile communications device via the mobile network and to the mobile communications device via the WLAN. A voice call is carried over one of the mobile network, the WLAN, and the PSTN depending on the quality of the data channels.

A fraud damage prevention system (1000) includes a call processing server (20) and an ENUM/DNS server (10). The call processing server (20) includes: a fraud determination requesting unit that transmits a fraud number determination request, to which a call originating number of a call originating device (4) is affixed, to the ENUM/DNS server (10), and that receives the determination result as to whether or not the call originating number is a fraud number; and a fraud prevention processing unit that performs a predetermined fraud damage prevention process. The ENUM/DNS server (10) includes: a storage unit that stores fraud number information (100); and a fraud determination unit that references the fraud number information (100) using the call originating number, determines that the call originating number is a fraud number in the case where a number that is the same as the call originating number is registered, and transmits the determination result to the call processing server (20).

A system and method are disclosed for training a machine-learning model to detect characteristics of fraudulent calls. The machine-learning model is trained using audio clips, voice recognition, call handler feedback and general public knowledge of commercial risks to detect and divert fraudulent calls, thereby alleviating the burdens otherwise placed on call center service representatives.

An example method of operation may include one or more of identifying an outbound call placed by a mobile device subscribed to a protected carrier network, determining the outbound call is destined for a destination telephone number that was stored in a call history of the mobile device, determining the destination telephone number is a scam call suspect telephone number based on one or more identified call filter parameters associated with the destination telephone number, and forwarding a scam call notification to the mobile device while the outbound call is dialing the destination telephone number.