Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for processing network simulations. In some embodiments, the method includes identifying, from one or more datasets, a plurality of microsegments; generating a graph neural network (GNN) comprising a plurality of nodes and a plurality of edges, where each node is associated with a microsegment; using a generative artificial intelligence (AI) engine to generate a marker token for each node, where the marker token is associated with an exposure level; executing at least one quantum algorithm, where executing the at least one quantum algorithm comprises processing a plurality of simulations; and based on one or more outputs of the quantum algorithm, causing a user device to execute a remedial action.

Abstract: Systems, computer program products, and methods are described herein for leveraging a distributed network system for secure data transfers. The present disclosure is configured to receive a data transfer from an entity where the entity is at least partially implicated within the data transfer, where the data transfer comprises participants; record the data transfer within a distributed network system, where the distributed network system comprises previous data transfers; verify identities of participants associated with the data transfer; and trigger the data transfer between participants.

Abstract: Systems, computer program products, and methods are described herein for digital resource allocation via an interactive computational framework. The present disclosure includes receiving credentials at a first endpoint device, authenticating the credentials, receiving a digital resource allocation request from the first endpoint device, the digital resource allocation request comprising parameters of a user associated with the authenticated credentials and item parameters of an item associated with the digital resource allocation request, retrieving user data associated with the user, determining, using a machine learning model, a digital resource allocation proposal based on the user data, generating, using a generative AI model, based on the digital resource allocation proposal, a smart contract comprising ownership, appending the smart contract to a distributed ledger, and transferring digital resources according to the smart contract.

Abstract: Systems, computer program products, and methods are described herein for robotic process automation (RPA) and machine learning (ML) for dynamic network monitoring and packet validation in electronic environments. The present disclosure is configured to extract data from network data packets received from at least one or more data sources and determine, using an ML engine, at least one network event trigger based on the data. The system also determines, using the ML engine, bot criteria of an RPA bot based on the at least one network event trigger. Also, the system assigns the RPA bot to execute a network transmission protocol based on at least the bot criteria and executes, via the RPA bot, a network transmission protocol.

Abstract: Systems, computer program products, and methods are described herein for computer network event data redundancy suppression via integrated machine learning. The present disclosure includes receiving interaction data from at least one endpoint device of a plurality of endpoint devices, recording the interaction data in usage logs, determining a presence of an event in the usage logs using at least one log analysis platform, enriching, using a lookup command of a network event management system, the event to form at least one enriched event, transmitting the at least one enriched event to a first machine learning model, determining a presence of a fractional event based on the at least one enriched event, grouping the fractional event with related fractional events into an aggregate event, determining a target endpoint device group for transmitting the aggregate event, and transmitting the aggregate event to the target endpoint device group.

Abstract: Systems, computer program products, and methods are described herein for resource tokenization using advanced computational models for data analysis and automated processing. The present disclosure is configured to receive a resource from a distributed network, wherein the distributed network is configured to digitize the resource. Further, the present disclosure is configured to execute a smart contract, wherein the smart contract is configured to automate execution of an agreement. Further still, the present disclosure is configured to transmit a notification to a user device to provide real-time access to the resource and resource data. Further still, the present disclosure is configured to configure, via an ownership engine, the resource data, wherein the ownership engine provides ownership transfers based on the agreement. Further still, the present disclosure is configured to implement, via an implementation engine, the resource on the distributed network.