Network allocation and monitoring engine using artificial intelligence and dynamic mapping

1 . A system for network allocation and monitoring engine using artificial intelligence and dynamic mapping, the system comprising:

a memory device with computer-readable program code stored thereon;

at least one processing device, wherein executing the computer-readable program code is configured to cause the at least one processing device to execute the computer-readable program code to:

extract data from network data packets received from at least one or more data sources;

identify, using an AI engine, network resources and network threats from the data;

generate, using the AI engine, at least one network attribute set based on at least the network resources and network threats;

determine and assign, using the AI engine, a weight for each of the at least one network attribute set;

generate, using the AI engine, a directed graph based on at least the network attribute set, wherein the directed graph is associated with at least one distributed network;

determine, using the AI engine, an internal network threshold based on at least the directed graph; and

generate, using the AI engine, an internal distributed network resources allocation based on at least the directed graph and the internal network threshold.

2 . The system of claim 1 , wherein executing the computer-readable program code is further configured to cause the at least one processing device to:

extract external data from external network data packets received from at least one external distributed network;

determine, using the AI engine, external network resources, external network threats, and an external distributed network resources allocation from the external data;

generate, using the AI engine, at least one external network attribute set based on at least the external network resources, external network threats, and the external distributed network resources allocation;

determine and assign, using the AI engine, an external weight for each of the at least one external network attribute set;

modify, using the AI engine, the directed graph based on at least the external network attribute set;

determine, using the AI engine, an external network threshold based on at least the directed graph and the external distributed network resources allocation; and

generate, using the AI engine, a comparison of the internal network threshold and the external network threshold.

3 . The system of claim 2 , wherein executing the computer-readable program code is further configured to cause the at least one processing device to:

receive the internal network threshold and the internal distributed network resources allocation for each at least one internal distributed network and the external distributed network resources allocation and the external network threshold for each at least one at least one external distributed network;

generate and assign, using the AI engine, a global internal distributed network weight based on at least the internal network threshold and the internal distributed network resources allocation for each at least one internal distributed network;

generate and assign, using the AI engine, a global external distributed network weight based on at least the external network threshold and the external distributed network resources allocation for each at least one external distributed network;

determine, using the AI engine, a global internal distributed network threshold based on at least the global internal distributed network weight;

determine, using the AI engine, a global external distributed network threshold based on at least the global external distributed network weight;

generate, using the AI engine, a global network threat map and a network comparison of the global internal distributed network threshold and the global external distributed network threshold; and

transmit a notification comprising the global network threat map and the network comparison.

4 . The system of claim 1 , wherein executing the computer-readable program code is further configured to cause the at least one processing device to:

generate a user interface on a display, wherein the user interface comprises at least one interactive mixed reality application;

render an interactive dashboard and the directed graph within the user interface; and

receive control signals from at least one device to navigate the user interface, wherein the control signals modify the interactive dashboard and the directed graph within the user interface.

5 . The system of claim 4 , wherein executing the computer-readable program code is further configured to cause the at least one processing device to:

render a plurality of directed graphs within the user interface, wherein the plurality of directed graphs comprises the directed graph and at least one additional directed graph associated with an additional distributed network;

receive control signals from the at least one device associated with the at least one interactive mixed reality application, wherein the control signals comprise interactions with at least one of one or more physical controls or one or more virtual objects; and

modify at least one view of the plurality of directed graphs within the user interface based on at least the control signals.

6 . The system of claim 2 , wherein executing the computer-readable program code is further configured to cause the at least one processing device to:

receive at least one historical dataset;

train the AI engine based on the at least one historical dataset, wherein the at least one historical dataset comprises at least one of historical network resources, historical network threats, historical network attribute sets, historical weights for historical network attribute sets, historical directed graphs associated with distributed networks, historical internal network thresholds, historical internal distributed network resources allocations, historical external network data packets, historical external network resources, historical external network threats, historical external network resources allocations, historical external network attribute sets, historical external network thresholds, historical directed graphs, or historical comparisons of the internal network threshold and the external network threshold;

receive network packet vulnerability data;

update the at least one historical dataset with the network packet vulnerability data; and

retrain the AI engine based on the network packet vulnerability data.

7 . The system of claim 1 , wherein executing the computer-readable program code is further configured to cause the at least one processing device to:

generate a network event forecast AI engine;

receive network event data associated with network switch checkpoints;

determining, using the network event forecast AI engine, a network event threshold based on the network event data associated with network switch checkpoints;

generate, using the network event forecast AI engine, revisions for the internal distributed network resources allocation based on at least the network event threshold; and

transmit the revisions for the internal distributed network resources allocation based on at least the network event threshold via notification.

8 . The system of claim 1 , wherein executing the computer-readable program code is further configured to cause the at least one processing device to:

determine, using the AI engine, an allocation threshold associated with the internal network threshold; and

generate at least one alert based on at least the allocation threshold.

9 . The system of claim 6 , wherein executing the computer-readable program code is further configured to cause the at least one processing device to:

access the network packet vulnerability data;

determine, using the AI engine, to delete the internal distributed network resources allocation based on at least the network packet vulnerability data; and

generate and transmit, using the AI engine, an alert comprising the internal distributed network resources allocation.

10 . The system of claim 1 , wherein executing the computer-readable program code is further configured to cause the at least one processing device to:

receive a request from at least one device, wherein the request comprises revised network data;

authenticate the request via multifactor authentication, wherein the multifactor authentication comprises at least two of a one-time password, a physical attribute authentication, authentication application, and authentication credentials;

revise, using the AI engine, at least one of the at least one network attribute set, the directed graph, and the internal network threshold based on the request, wherein the request comprises updating the network resources and network threats;

generate, using the AI engine a revised internal distributed network resources allocation; and

transmit the revised internal distributed network resources allocation via notification.

11 . A computer program product for network allocation and monitoring engine using artificial intelligence and dynamic mapping, wherein the computer program product comprises at least one non-transitory computer-readable medium having computer-readable program code portion embodied therein, the computer-readable program code portions which when executed by a processing device are configured to cause a processor to:

extract data from network data packets received from at least one or more data sources;

identify, using an AI engine, network resources and network threats from the data;

generate, using the AI engine, at least one network attribute set based on at least the network resources and network threats;

determine and assign, using the AI engine, a weight for each of the at least one network attribute set;

generate, using the AI engine, a directed graph based on at least the network attribute set, wherein the directed graph is associated with at least one distributed network;

determine, using the AI engine, an internal network threshold based on at least the directed graph; and

generate, using the AI engine, an internal distributed network resources allocation based on at least the directed graph and the internal network threshold.

12 . The computer program product of claim 11 , wherein the processing device is further configured to:

extract external data from external network data packets received from at least one external distributed network;

determine, using the AI engine, external network resources, external network threats, and an external distributed network resources allocation from the external data;

generate, using the AI engine, at least one external network attribute set based on at least the external network resources, external network threats, and the external distributed network resources allocation;

determine and assign, using the AI engine, an external weight for each of the at least one external network attribute set;

modify, using the AI engine, the directed graph based on at least the external network attribute set;

determine, using the AI engine, an external network threshold based on at least the directed graph and the external distributed network resources allocation; and

generate, using the AI engine, a comparison of the internal network threshold and the external network threshold.

13 . The computer program product of claim 12 , wherein the processing device is further configured to:

receive the internal network threshold and the internal distributed network resource allocation for each at least one internal distributed network and the external distributed network resources allocation and the external network threshold for each at least one at least one external distributed network;

generate and assign, using the AI engine, a global internal distributed network weight based on at least the internal network threshold and the internal distributed network resources allocation for each at least one internal distributed network;

generate and assign, using the AI engine, a global external distributed network weight based on at least the external network threshold and the external distributed network resources allocation for each at least one external distributed network;

determine, using the AI engine, a global internal distributed network threshold based on at least the global internal distributed network weight;

determine, using the AI engine, a global external distributed network threshold based on at least the global external distributed network weight;

generate, using the AI engine, a global network threat map and a network comparison of the global internal distributed network threshold and the global external distributed network threshold; and

transmit a notification comprising the global network threat map and the network comparison.

14 . The computer program product of claim 11 , wherein the processing device is further configured to:

generate a user interface on a display, wherein the user interface comprises at least one interactive mixed reality application;

render an interactive dashboard and the directed graph within the user interface; and

receive control signals from at least one device to navigate the user interface, wherein the control signals modify the interactive dashboard and the directed graph within the user interface.

15 . The computer program product of claim 11 , wherein the processing device is further configured to:

generate a network event forecast AI engine;

receive network event data associated with network switch checkpoints;

determining, using the network event forecast AI engine, a network event threshold based on the network event data associated with network switch checkpoints;

generate, using the network event forecast AI engine, revisions for the internal distributed network resources allocation based on at least the network event threshold; and

transmit the revisions for the internal distributed network resources allocation based on at least the network event threshold via notification.

16 . The computer program product of claim 11 , wherein the processing device is further configured to:

determine, using the AI engine, an allocation threshold associated with the internal network threshold; and

generate at least one alert based on at least the allocation threshold.

17 . The computer program product of claim 11 , wherein the processing device is further configured to:

receive a request from at least one device, wherein the request comprises revised network data;

authenticate the request via multifactor authentication, wherein the multifactor authentication comprises at least two of a one-time password, a physical attribute authentication, authentication application, and authentication credentials;

revise, using the AI engine, at least one of the at least one network attribute set, the directed graph, and the internal network threshold based on the request, wherein the request comprises updating the network resources and network threats;

generate, using the AI engine a revised internal distributed network resources allocation; and

transmit the revised internal distributed network resources allocation via notification.

18 . A computer-implemented method for network allocation and monitoring engine using artificial intelligence and dynamic mapping:

extracting data from network data packets received from at least one or more data sources;

identifying, using an AI engine, network resources and network threats from the data;

generating, using the AI engine, at least one network attribute set based on at least the network resources and network threats;

determining and assigning, using the AI engine, a weight for each of the at least one network attribute set;

generating, using the AI engine, a directed graph based on at least the network attribute set, wherein the directed graph is associated with at least one distributed network;

determining, using the AI engine, an internal network threshold based on at least the directed graph; and

generating, using the AI engine, an internal distributed network resources allocation based on at least the directed graph and the internal network threshold.

19 . The computer-implemented method of claim 18 , wherein the computer-implemented method is further configured for:

extracting external data from external network data packets received from at least one external distributed network;

determining, using the AI engine, external network resources, external network threats, and an external distributed network resources allocation from the external data;

generating, using the AI engine, at least one external network attribute set based on at least the external network resources, external network threats, and the external distributed network resources allocation;

determining and assigning, using the AI engine, an external weight for each of the at least one external network attribute set;

modifying, using the AI engine, the directed graph based on at least the external network attribute set;

determining, using the AI engine, an external network threshold based on at least the directed graph, and the external distributed network resources allocation; and

generating, using the AI engine, a comparison of the internal network threshold and the external network threshold.

20 . The computer-implemented method of claim 18 , wherein the computer-implemented method is further configured for:

generating a user interface on a display, wherein the user interface comprises at least one interactive mixed reality application;

rendering an interactive dashboard and the directed graph within the user interface; and

receiving control signals from at least one device to navigate the user interface, wherein the control signals modify the interactive dashboard and the directed graph within the user interface.