Abstract: Methods, apparatuses, and computer program products are disclosed for account verification. An example method includes receiving a request for payment transmission from a payment provider device and generating a first verification element for a payment destination device associated with the request for payment transmission. The method further includes transmitting the first verification element to the payment destination device via a first real-time payment message. In an instance in which the computing device receives responsive authorization from the payment destination device, the method includes verifying the payment destination device. In response to verifying the payment destination device, the method includes transmitting a first real-time payment to the payment destination device. In an instance in which the computing device fails to receive responsive authorization from the payment destination device, the method includes transmitting a verification failure notification to the payment provider device.

Abstract: Data privacy using federated learning is provided by receiving one or more instance(s) of a master RL agent model and training the instance(s) of the master RL agent model on a corresponding graph, thereby generating corresponding sets of RL model weights. One or more information gains corresponding to one or more software stacks are generated. The information gain(s) and the RL model weight(s) are transmitted to a central server to enable the central server to update the master RL agent model based on the information gain(s) and the RL model weights.

Abstract: Methods, systems and computer program products are provided for optimizing resources privately. An initial information gain corresponding to an initial client embedding dataset is computed and a machine learning model is trained based on the initial client embedding data set to generate at least one initial attack path in the initial graph data. A second information gain corresponding to a second client embedding data set is computed. A difference between the first information gain and the second information gain is computed. The machine learning model is trained if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data.

Abstract: Methods, systems and computer program products are provided for integrating risk and threat intelligence from various sources, to provide real-time awareness of potential threats to a computer network, which are now described herein in terms of an example enterprise system.