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.

A method for authenticating a passive RFID tag includes acquiring a tag fingerprint of a first tag as a first tag fingerprint, the first tag being the genuine tag; acquiring a tag fingerprint of a second tag as a second tag fingerprint, the second tag being the tag to be authenticated; comparing the first tag fingerprint with the second tag fingerprint: if the first tag fingerprint is consistent with the second tag fingerprint, determining that the second tag is a genuine tag, otherwise determining the second tag is a forged tag. The tag fingerprint is the persistence time enabling the passive RFID tag to operate normally during discharge after fully charging. The beneficial effects include being high in robustness to the change of environment and high in authentication accuracy and capable of being directly deployed on an existing commercial RFID device without modifying hardware of the tag and reader.

Systems, methods, and computer-readable media for facilitating an authentication processing service are provided.

The present invention relates to a method and system of cybersecurity; and particularly relates to an encryption method and system on the basis of cognitive computing for xenomorphic cryptography or unusual form of cryptography; said method comprises generating a Functional Neural Network or KeyNode (KN) of the system by programming a chain of multiple nodes also called Artificial Mirror Neurons (AMN) based on captured information of reaction time and emotional response to a simple task; racing the nodes in the Functional Neural Network or KeyNode (KN) as an encryption device or cipher for the time of use; generating a password at the time of use based on the sum of intrinsic values of the nodes in the racing network at this time and adopting the generated password for authentication. The present invention can be applied to secure online and mobile communication especially at the dawn of 5G with generalization of open API lifestyle platforms so as to allow real-time identification for digital cryptocurrency payments and other public distributed ledger technology (DLT) mechanisms.

A method performed by a user device is disclosed. The method comprising generating a secret and measuring a biometric template of a user operating the user device. The method then generates a plurality of secret shares of the secret and of the biometric template. The user device then transmits the secret shares of the secret and of the biometric template to a plurality of recovery devices. After, the user device may then initiate a recovery of the secret and measure a biometric measurement of the user. Data of the biometric measurement may be transmitted to the plurality of recovery devices, where the recovery devices perform a partial computation. The user device use the plurality of partial computations to determine a match between the biometric template and the biometric measurement. If the two biometrics match, the user device can reconstruct the secret using shares of the secret from the recovery devices.

A distributed identity system with local identification includes an identity system device and at least one local electronic device. The local electronic device locally stores at least a portion of identity information and the biometric identification information stored by the identity system device. The local electronic device determines identities by comparing received digital representations of biometrics with locally stored biometric identification information, performs actions using locally stored identity information included in the local copy, and uploads data related to the actions to the identity system device upon occurrence of an upload condition.

According to the present invention, there is provided a sales system (10) that includes a plurality of information acquisition units (11-1 to 11-N (N is an integer equal to or greater than 2)) installed for a plurality of respective products, a registration unit (12) that registers a product corresponding to a first information acquisition unit (11-A (A is an integer equal to or greater 1 and equal to or smaller than N)) in association with customer identification information when the customer identification information is input through the first information acquisition unit (11-A), a reception unit (13) that receives a request for a product list associated with the customer identification information from a customer terminal, and a transmission unit (14) that transmits the product list to the customer terminal in response to the request.

An electronic device is provided. The electronic device includes a communication interface including circuitry, a memory, and a processor which, based on receiving ID information generated by performing a first encryption process on biometric information and password information generated by performing a second encryption process on the biometric information from an external electronic device through the communication interface, is configured to control the electronic device to: store the ID information and the password information in the memory. The processor, based on receiving first ID information and first password information from the external electronic device, is configured to control the electronic device to: acquire at least one candidate ID information from the memory based on the first ID information, compare password information corresponding to each of the acquired at least one candidate ID information with the first password information to identify one of the candidate ID information, and perform user authentication based on the identified candidate ID information and corresponding password information.

A secure access control system configured to control access to sensitive data stored on disparate systems is disclosed. A first entity is designated to control access to second entity data. An authentication token, generated using a key derivation function, is used to authenticate the first entity. The authenticated first entity is granted access to second entity data. An access control interface is generated configured to selectively grant or withdraw access to second entity data. The access control interface identifies entities associated with respective access controls. The access control interface is instantiated on a first entity device. Activation indications of access controls is received over a network. Access to second entity data is accordingly granted or withdrawn. Access control transition event rules and/or access control transition time rules are retrieved. Using monitored events and the access control transition event rules, and/or a monitored current time and the access control transition time rules, a determination is made as to transition access control of the second entity data first entity to the second entity.

An apparatus and method for joining an Internet of Things (IoT) network are provided, the apparatus including a communicator configured to receive, from an electronic device, an encrypted auto-onboard configuration data associated with the IoT network, a sensor configured to detect a user command, and at least one processor configured to generate a decryption key based on features extracted from the user command, decrypt the encrypted auto-onboard configuration data using the decryption key, and join the IoT network.