Abstract:
A new approach is proposed to support communication fraud detection and prevention by utilizing an artificial intelligence (AI) engine that detects and blocks impersonation attacks in real time. The AI engine automatically collects all historical electronic messages of each individual user in the entity on an electronic messaging system via an application programming interface (API) call to the electronic messaging system. The AI engine then analyzes the collected electronic messages for a plurality of features to identify unique communication patterns of users in the entity via AI-based classification. When one or more related incoming messages are retrieved in real time, the identified communication patterns are utilized to detect anomalous signals in metadata and/or content of the incoming messages. The AI engine then identifies with a high degree of accuracy whether the incoming messages are part of an impersonation attack based on the detected anomalous signals.
Abstract:
A device includes a database, a controller, and a PVN router. The database is configured to store network settings information and tracks devices connected to a network. The controller is configured to control access of devices to one another after establishing a connection to the network. The PVN router is configured to receive a provisioning request from a requesting to connect to the network. The PVN router is further configured to transmit a provisioning response to the requesting device based on instantiation of a PVN template received from the database. The PVN template is generated based on the network settings information and further based on the control access determined by the controller. The provisioning response establishes a connection between the requesting device and the network. The requesting device is inaccessible by a subset of devices already connected in the network after the connection is established and vice versa.
Abstract:
A device includes a database, a controller, and a PVN router. The database is configured to store network settings information and tracks devices connected to a network. The controller is configured to control access of devices to one another after establishing a connection to the network. The PVN router is configured to receive a provisioning request from a requesting to connect to the network. The PVN router is further configured to transmit a provisioning response to the requesting device based on instantiation of a PVN template received from the database. The PVN template is generated based on the network settings information and further based on the control access determined by the controller. The provisioning response establishes a connection between the requesting device and the network. The requesting device is inaccessible by a subset of devices already connected in the network after the connection is established and vice versa.
Abstract:
A new approach is proposed to support anti-fraud user training and protection by identifying and training individuals within an entity who are at high risk of being targeted in an impersonating attack. An AI engine automatically collects historical electronic messages of each individual in the entity on an electronic messaging system via an application programming interface (API) call. The AI engine then analyzes contents the collected historical electronic messages and calculates a security score for each individual via AI-based classification. The AI engine identifies high-risk individuals within the entity based on their security scores and launches simulated impersonating attacks against these individuals to test their security awareness. The AI engine then collects and analyzes responses to the simulated attacks by those high-risk individuals in real time to identify issues in the responses and to take corresponding actions to prevent the high-risk individuals from suffering damages in case of real attacks.
Abstract:
A device includes a database, a controller, and a PVN router. The database is configured to store network settings information and tracks devices connected to a network. The controller is configured to control access of devices to one another after establishing a connection to the network. The PVN router is configured to receive a provisioning request from a requesting to connect to the network. The PVN router is further configured to transmit a provisioning response to the requesting device based on instantiation of a PVN template received from the database. The PVN template is generated based on the network settings information and further based on the control access determined by the controller. The provisioning response establishes a connection between the requesting device and the network. The requesting device is inaccessible by a subset of devices already connected in the network after the connection is established and vice versa.
Abstract:
A device includes a database, a controller, and a PVN router. The database is configured to store network settings information and tracks devices connected to a network. The controller is configured to control access of devices to one another after establishing a connection to the network. The PVN router is configured to receive a provisioning request from a requesting to connect to the network. The PVN router is further configured to transmit a provisioning response to the requesting device based on instantiation of a PVN template received from the database. The PVN template is generated based on the network settings information and further based on the control access determined by the controller. The provisioning response establishes a connection between the requesting device and the network. The requesting device is inaccessible by a subset of devices already connected in the network after the connection is established and vice versa.
Abstract:
A new approach is proposed to support electronic messaging threat scanning and detection to identify security threats missed by an existing security software of an electronic messaging system. An AI engine first retrieves an entire inventory of historical electronic messages by the users on the electronic messaging system over a certain time. The AI engine scans the retrieved inventory of historical electronic messages to identify various types of security threats to the electronic messaging system in the past. The AI engine compares the identified security threats to those that have been identified by the existing security software to identify a set of security threats that had eluded or missed by the existing security software in the past. The AI engine then removes, modifies, or quarantines electronic messages that contain the missed security threats so that none of them will trigger an attack to the electronic messaging system in the future.