Abstract: A system for implementing auto-correction to solve dynamic issues in a distributed network comprises a processor associated with a server. The processor detects an application issue at a network node. The application issue comprises a user request with an issue statement and a user interaction associated with one or more operation parameters of an application. The processor receives a set of data objects associated with the application issue. The processor classifies the data objects into one or more issue patterns by a machine learning model. The processor processes the one or more issue patterns and application information through a neural network to determine executable operations configured to solve the application issue. In response to determining that the application is running at the network node, the processor deploys the executable operations to the network node to correct the one or more parameters of the application to prevent a failure application.

Abstract: A system for auto-detecting dynamic issue changes in a distributed network comprises a processor associated with a server. The processor detects an application issue at a network node receives a first set of data objects associated with the application issue occurring at the first timestamp. The processor receives a second set of data objects associated with the application issue occurring at the second timestamp. The processor determines a change between a first set of the data objects and a second set of data objects. The processor identifies an issue pattern represents an operation change of the application by a machine learning model. The processor processes the issue pattern with application information through a neural network to determine a series of executable operations associated with the application issue. The processor deploys the series of the executable operations to solve the application issue to prevent a failure operation of the application.

Abstract: A system for auto-detecting dynamic issue changes in a distributed network comprises a processor associated with a server. The processor detects an application issue at a network node receives a first set of data objects associated with the application issue occurring at the first timestamp. The processor receives a second set of data objects associated with the application issue occurring at the second timestamp. The processor determines a change between a first set of the data objects and a second set of data objects. The processor identifies an issue pattern represents an operation change of the application by a machine learning model. The processor processes the issue pattern with application information through a neural network to determine a series of executable operations associated with the application issue. The processor deploys the series of the executable operations to solve the application issue to prevent a failure operation of the application.

Abstract: A system for implementing auto-correction to solve dynamic issues in a distributed network comprises a processor associated with a server. The processor detects an application issue at a network node. The application issue comprises a user request with an issue statement and a user interaction associated with one or more operation parameters of an application. The processor receives a set of data objects associated with the application issue. The processor classifies the data objects into one or more issue patterns by a machine learning model. The processor processes the one or more issue patterns and application information through a neural network to determine executable operations configured to solve the application issue. In response to determining that the application is running at the network node, the processor deploys the executable operations to the network node to correct the one or more parameters of the application to prevent a failure application.

Abstract: A system for auto-determining solutions for dynamitic issues comprises a processor associated with a server. The processor detects an application issue associated with an application running at a network node in a distributed network. The processor receives a set of data objects associated with the application issue. The processor classifies the set of the data objects of the application issue into one or more issue patterns using a machine learning model. The machine learning model is trained based on a plurality of sets of data objects and issue patterns associated with corresponding previous application issues. The processor processes the one or more issue patterns and application information through a neural network to determine a series of executable operations for solving the application issue. The processor deploys the series of the executable operations to solve the application issue occurring at the network node to prevent a failure operation of the application.

Abstract: Systems, computer program products, and methods are described herein for generation of data content based on learning reinforcement. The present invention is configured to receive a video file demonstrating regulatory compliance requirements; display the video file in one or more interactive application environments stored thereon; initiate a reinforcement learning algorithm on the video file; initiate an optimization policy generation engine on the user inputs to generate an optimization policy, wherein the optimization policy generation engine is configured to encode the one or more user inputs into shaping rewards; initiate an implementation of the optimization policy on the video file to generate a modified video file based on at least the optimization policy; initiate a validation engine on the modified video file to validate one or more changes implemented on the video file; and initiate a deployment of the modified video file to the one or more users.

Abstract: Systems, computer program products, and methods are described herein for scalable encryption framework using virtualization and adaptive sampling. The present invention is configured to receive metadata associated with one or more intrusion types from an intrusion data lake; initiate an adaptive instance sampling engine on the metadata associated with the one or more intrusion types to generate a sampled intrusion data lake; initiate one or more simulations of atomic intrusion on a firewall; generate one or more prioritized combination of the one or more sampled intrusion types; initiate one or more simulations of cumulative intrusion on the firewall using the one or more prioritized combination of the one or more sampled intrusion types; determine an atomic performance metric and a cumulative performance metric of the firewall; and generate a robustness report for the firewall.

Abstract: Systems, computer program products, and methods are described herein for scalable encryption framework using virtualization and adaptive sampling. The present invention is configured to receive metadata associated with one or more intrusion types from an intrusion data lake; initiate an adaptive instance sampling engine on the metadata associated with the one or more intrusion types to generate a sampled intrusion data lake; initiate one or more simulations of atomic intrusion on a firewall; generate one or more prioritized combination of the one or more sampled intrusion types; initiate one or more simulations of cumulative intrusion on the firewall using the one or more prioritized combination of the one or more sampled intrusion types; determine an atomic performance metric and a cumulative performance metric of the firewall; and generate a robustness report for the firewall.

Abstract: Systems, computer program products, and methods are described herein for generation of data content based on learning reinforcement.