Cognitive modeling system

1. A cognitive modeling system comprising:

a receiver configured to receive a set of actors and associated actor information and receive assets and their associated asset information;

a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries for a time period, wherein the taxonomy comprises a tree-like structure that organizes data according to an information category provided for each level of the tree-like structure; and

a computing apparatus configured to compute a trust value of the cognitive model as a fuzzy number and activate the cognitive model if the trust value of the cognitive model is above a cognitive model trust threshold, wherein the fuzzy number measures degree of nearness to a threshold, hard and soft violations, and degree of a violation;

wherein when the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model, wherein the at least one anomaly analysis comprises determining a probability representing a degree to which a plurality of actions by an actor at a plurality of times are within expected limits, the probability computed as a fuzzy number;

wherein, for-at least one data dictionary entry, the cognitive modeling system is configured to normalize associated actor actions by converting at least one event to data dictionary format, insert at least one normalized terrain entry into the cognitive model, and update the cognitive model.

2. The cognitive modeling system of claim 1 , wherein the computing apparatus comprises a series of node devices, comprising a series of processor nodes configured to provide data to a series of aggregator nodes.

3. The cognitive modeling system of claim 1 , wherein the anomaly analysis determines a severity and degree of inconsistency from normal behavior.

4. The cognitive modeling system of claim 1 , wherein the cognitive modeling system is further configured to perform a threat detection comprising an importance value assigned to a resource.

5. The cognitive modeling system of claim 1 , wherein the computing apparatus normalizes, migrates, supervises and analyzes data to identify issues between actors in the taxonomy.

6. The cognitive modeling system of claim 2 , wherein the taxonomy comprises a common language understood by the series of node devices such that data can be shared between the series of node devices.

7. A cognitive modeling system comprising:

a receiver configured to receive a set of actors, associated actor information, assets, and associated asset information;

a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries applicable to a time period, wherein the taxonomy comprises a tree-like structure that organizes data according to an information category provided for each level of the tree-like structure; and

a computing apparatus configured to compute a trust value of the cognitive model as a fuzzy number and activate the cognitive model if the trust value of the cognitive model is above a cognitive model trust threshold;

wherein the fuzzy number measures degree of nearness to a threshold, hard and soft violations, and degree of a violation;

wherein the computing apparatus computes the trust value using at least one anomaly analysis comprising determining a probability representing a degree to which a plurality of actions by an actor at a plurality of times are within expected limits, the probability computed as a fuzzy number.

8. The cognitive modeling system of claim 7 , wherein when the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model.

9. The cognitive modeling system of claim 7 , wherein, for at least one data dictionary entry, the cognitive modeling system is configured to normalize associated actor actions by converting at least one event to data dictionary format.

10. The cognitive modeling system of claim 7 , wherein, for at least one data dictionary entry, the cognitive modeling system is configured to insert at least one normalized terrain entry into the cognitive model.

11. The cognitive modeling system of claim 7 , wherein, for at least one data dictionary entry, the cognitive modeling system is configured to update the cognitive model.

12. The cognitive modeling system of claim 7 , wherein the computing apparatus comprises a series of node devices, comprising a series of processor nodes configured to provide data to a series of aggregator nodes.

13. The cognitive modeling system of claim 7 , wherein the anomaly analysis determines a severity and degree of inconsistency from normal behavior.

14. The cognitive modeling system of claim 7 , wherein the cognitive modeling system is further configured to perform a threat detection comprising an importance value assigned to a resource.

15. The cognitive modeling system of claim 7 , wherein the computing apparatus normalizes, migrates, supervises and analyzes data to identify issues between actors in the taxonomy.

16. A cognitive modeling method comprising:

receiving at a hardware computing arrangement, comprising a number of processor nodes and a number of aggregator nodes, a set of actors, associated actor information, assets, and associated asset information;

creating data dictionary entries for a taxonomy based on the set of actors and the assets, wherein the taxonomy comprises a tree-like structure that organizes data according to an information category provided for each level of the tree-like structure;

creating a cognitive model using the data dictionary entries for a time period;

computing a trust value of the cognitive model as a fuzzy number;

activating the cognitive model if the trust value of the cognitive model is above a cognitive model trust threshold;

when the cognitive model is activated, scheduling a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model, wherein the at least one anomaly analysis comprises determining a probability representing a degree to which a plurality of actions by an actor at a plurality of times are within expected limits, the probability computed as a fuzzy number; and

for at least one data dictionary entry, normalizing associated actor actions by:

converting at least one event to data dictionary format;

inserting at least one normalized terrain entry into the cognitive model; and

updating the cognitive model;

wherein the fuzzy number measures degree of nearness to a threshold, hard and soft violations, and degree of a violation.

17. The cognitive modeling method of claim 16 , wherein anomaly analysis determines a severity and degree of inconsistency from normal behavior.

18. The cognitive modeling method of claim 16 , further comprising performing a threat detection comprising an importance value assigned to a resource.

19. The cognitive modeling method of claim 16 , wherein the taxonomy comprises a common language understood by the series of node devices such that data can be shared between the series of node devices.