Cognitive modeling system

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.

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.