Filtering incoming calls according to predicted preferences of a user. User preferences are predicted by analysis of user behavior, online activity, oral queues, and purchasing history. Data analysis includes weighting caller and user attributes according to a scheme that is dynamically updated by applying user feedback and/or machine learning processes.

There are provided a method and a system for detecting and blocking spam activity in a cloud system. The method can be executed at a server. The method comprises receiving from a first user of the plurality of users of the cloud service an indication of a first sharing action for sharing access to the digital object; responsive to the first indication, increment a sharing counter value of a sharing counter dedicated to the digital object; receiving from a second user of the plurality of users of the cloud service an indication of a second sharing action for sharing access to the digital object; responsive to the second indication further incrementing the sharing counter value of the sharing counter dedicated to the digital object; responsive to the sharing counter value reaching a pre-determined threshold value, executing a spam preventive action associated with the digital object.

The present disclosure discloses a method for preventing a SIP device from being attacked, a calling device, and a called device, belonging to the field of network security. The present disclosure provides a method including: generating, by a calling device and a called device, the same public password, and transmitting, by the calling device, a connection request to the called device; performing, by the called device, header field verification on the connection request to verify whether a specified header field is carried in the connection request; performing, by the called device, device verification on the connection request; and performing, by the called device, identity verification on the connection request, and establishing, by the called device, a connection to the calling device. In this case, spoofing data is filtered out and the SIP device is not easily attacked, so that a user is free of disturbance.

A device receives call information associated with a call from a first user device to a second user device, where the first user device is associated with a first network, and the second user device is associated with a second network separate from the first network. The call information includes a caller identification and is received via an originating network device of the first network. The device determines whether the caller identification is verified, and adds authentication information to the call information when the caller identification is verified. The device receives the call information and the authentication information from a terminating network device of the first network, and removes the authentication information from the call information. The device adds a cryptographic signature to the call information, and causes the call information and the cryptographic signature to be provided to the second network for routing to the second user device.

A system described herein may provide a technique for Embodiments described herein provide for the use of machine learning, artificial intelligence, and/or other techniques for network-implemented spam call detection. Calls may be screened prior to notifying a called User Equipment (“UE”) that a call has been placed to the called UE. A Machine Learning Spam Detection Component (“MLSDC”) may screen a call, such as a voice call, by initiating a call session between the MLSDC and a calling UE, from which the call was requested. Via the established call session, the MLSDC may receive communications, such as voice communications, from the UE, and may determine a measure of likelihood that the call request is associated with spam by using machine learning or other techniques to compare the received communications against one or more models that indicate attributes of calls that have been identified as spam.

One example method of operation may include identifying call data associated with a received call, identifying call parameters from the call data, and the call parameters include one or more call routing parameters associated with call routing of the call and one or more call session parameters associated with a call session of the call, assigning weights to one or more of the call routing parameters and the call session parameters, determining a scam score for the call based on a sum of the weights applied to the call routing parameters and the call session parameters, and blocking the call when the scam score is greater than or equal to a predetermined threshold scam score.

Disclosed herein are systems and methods for providing mobile call authentication. For instance, a token indicative of a call request can be received from a calling party. The token can include a called party number and a time of the request. A subscriber database can be accessed to determine identifying information associated with the calling party based at least in part on the token. The token can be authenticated based at least in part on the identifying information and using one or more predefined authentication protocols. The token can be stored in a call session registry storing data indicative of a plurality of active telephone call events. A verification request for the call request can be received from the called party. The call request can be verified based at least in part on the token. The called party can be notified that the call request has been verified.

Novel tools and techniques are provided for implementing web-based monitoring and detection of fraudulent or unauthorized use of voice calling service. In various embodiments, a computing system might receive, from a user device associated with an originating party, a request to initiate a call session with a destination party, the request comprising user information associated with the originating party and a destination number associated with the destination party; might query a database with session data (including user information) to access permission data and configuration data; and might configure fraud logic using received configuration data from the database. The computing system might analyze the session data and permission data using the configured fraud logic to determine whether the originating party is permitted to establish the requested call session with the destination party; if so, might initiate one or more first actions; and, if not, might initiate one or more second actions.

A device, system and method for routing botnet calls to a botnet call-answer queue. A device, such as a call answering point (CAP) and/or a public-safety answering point (PSAP) router device, receives a call and determines an audio signature of the call. The device compares the audio signature of the call with one or more botnet audio signatures stored at a memory. In response to the audio signature of the call matching at least one of the one or more botnet audio signatures, the device: identifies the call as a botnet call that has been placed by a botnet; and cause the call to be routed to a botnet call-answer queue.

Disclosed herein is phone call endpoint security. In particular, the embodiments provide a mechanism to generate or modify a Session Initiation Protocol (SIP) invite message to include a phone number and an encrypted identifier that identifies a calling device. A network computing device decrypts the encrypted identifier and queries a database that correlates phone numbers to identifiers. The network computing device determines to forward or reject the SIP invite message based on whether the identifier and the phone number in the SIP invite message are correlated to one another in the database. Accordingly, the endpoint is secured, and calling devices are blocked from attempting to make deceptive phone calls from phone numbers not known to be associated with the calling device.