A method and a User Equipment, UE (120) for detecting that the UE has received a fraudulent missed call, e.g. from a non-legitimate device (150). When receiving a missed call which is ended before a user of the UE has answered the incoming call, the UE determines the duration of the missed call, and indicates, e.g. to a user of the UE, the 5 duration of the missed call. The missed call may be determined as potentially fraudulent if the duration of the missed call is below or equal to a predetermined threshold.

Aspects of the subject disclosure may include, for example, a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations including receiving a call; selecting a next carrier to handoff the call; generating a call data record (CDR) for the handoff to the next carrier; encrypting the CDR using a call encryption key, thereby creating an encrypted CDR; encrypting the encrypted CDR using a committee encryption key, thereby creating a double encrypted CDR; recording the double encrypted CDR to a blockchain; and sending the call encryption key to the next carrier. Other embodiments are disclosed.

A processing system may maintain a communication graph that includes nodes representing a plurality of phone numbers including a first phone number and edges between the nodes representing a plurality of communications between the plurality of phone numbers and may generate at least one vector via a graph embedding process applied to the communication graph, the at least one vector representing features of at least a portion of the communication graph. The processing system may then apply the at least one vector to a prediction model that is implemented by the processing system and that is configured to predict whether the first phone number is associated with a type of network activity associated with a telecommunication network and may implement a remedial action in response to an output of the prediction model indicating that the first phone number is associated with the type of network activity.

A processing system may maintain a relationship graph that includes nodes and edges representing phone numbers and device identifiers having associations with the phone numbers. The processing system may obtain an identification of a first phone number or a first device identifier for a fraud evaluation and extract features from the relationship graph associated with at least one of the first phone number or the first device identifier. The plurality of features may include one or more device identifiers associated with the first phone number, or one or more phone numbers associated with the first device identifier. The processing system may then apply the features to a prediction model that is implemented by the processing system and that is configured to output a fraud risk value of the first phone number or the first device identifier and implement at least one remedial action in response to the fraud risk value.

A method and system for the purchase and activation of services on a wireless device are provided. The method and system include the use of an airtime card with a near field communication tag with a unique identifier code used to improve a user's experience and ease of activation/provisioning of services for the wireless device. In some aspects, the unique identifier may be generated during the activation and purchase of the wireless service and it is not required that the unique identifier code be recorded in a backend system of the wireless provider prior to activation. The services to be purchased and activated may include at least one of the following: out of the box activation, device upgrades, device reactivations, wireless number changes, wireless number porting, and the addition or purchasing of services including airtime, data, and/or SMS enrollments or data content.

One embodiment of the described invention is directed to a key management module deployed within a cybersecurity system that operates as a multi-tenant Security-as-a-Service (SaaS) by relying on Infrastructure-as-a-Service (IaaS) cloud processing resources and cloud storage resources. The key management module is configured to assign a master key to a subscriber upon registration and, as requested, generate one or more virtual keys, based at least in part on the master key, for distribution to the subscriber. Each virtual key is included as part of a submission into the cybersecurity system and is used to authenticate the subscriber of the submission and verify that the subscriber is authorized to perform one or more tasks associated with the submission before the one or more tasks are performed.

When an SIP server receives a call signal addressed to a callee side terminal, the SIP server transmits a warning signal to the callee side terminal before transmitting the call signal to the callee side terminal when an international call identifier indicating that the call signal passes through a foreign country is set in the call signal. In addition, when the SIP server receives the call signal addressed to the callee side terminal, the SIP server includes a warning that the caller number is disguised in a warning signal when an international call identifier indicating that the call signal passes through a foreign country is set in the call signal and the caller number of the call signal is a number from a country in which the callee side terminal is located.

Aspects of the subject disclosure may include, for example, a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations including receiving a call; selecting a next carrier to handoff the call; generating a call data record (CDR) for the handoff to the next carrier; encrypting the CDR using a call encryption key, thereby creating an encrypted CDR; encrypting the encrypted CDR using a committee encryption key, thereby creating a double encrypted CDR; recording the double encrypted CDR to a blockchain; and sending the call encryption key to the next carrier. Other embodiments are disclosed.

As described herein, a machine learning system, method, and computer program are provided for making payment related customer predictions using remotely sourced data. A system of a communication service provider (CSP) identifies a customer of the CSP. Additionally, the system collects data from a plurality of data sources independent from the CSP, the data including telephone numbers and/or webpage URLs of other services providers that are associated with making payments. Further, the system processes the collected data to form input data indicating which of the telephone numbers were contacted by the customer and/or webpage URLs were accessed by the customer. Still yet, the system processes the input data using at least one machine learning algorithm to make at least one payment related prediction for the customer. Moreover, the system outputs the at least one payment related prediction made for the customer.

Systems and methods for managing a set of electronic assets from a single location are disclosed. The method includes providing a portal with a network security access control. The method includes determining that login credentials input to the access control are associated with a set of electronic assets corresponding to a plurality of third-party computing systems with application programming interface (API) gateways configured to accept API calls directed to changes in functionality of the electronic assets. The method includes presenting, via the portal, a virtual icon to identify a coordinated action with respect to the set of electronic assets and, in response to a selection of the virtual icon, executing a set of API calls that include an asset-specific API call to each third-party computing system in the plurality of third-party computing systems to implement the coordinated action on all electronic assets in the set of electronic assets.