Computer-based systems configured to select a monitored data segmentation and methods of use thereof

A computer-implemented method comprising:

• identifying, by at least one processor, at least one device capable of processing a multi-dimensional data stream;
• dynamically calculating, by the at least one processor, a plurality of hash keys respectively for a plurality of monitored segmentations associated with the at least one device capable of processing the multi-dimensional data stream, the plurality of monitored segmentations generated based on at least one feature;
• generating, by the at least one processor, at least one increment data counter that corresponds to each hash key of the plurality of hash keys in a plurality of counting structures,
• wherein each of the plurality of counting structures comprises:
• a current counting structure, counting the at least one incremental data counter during a current time period, and
• a total counting structure, counting the at least one incremental data counter during a total time period;
• dynamically calculating, by the at least one processor, an anomaly score for each of the plurality of monitored segmentations based on at least one value of corresponding one of the plurality of counting structures to generate a plurality of anomaly scores for the plurality of monitored segmentations;
• utilizing, by the at least one processor, a machine learning module to predict a modification to a calculated anomaly score associated with a selected monitored segmentation of the plurality of monitored segmentations based on receiving additional information;
• and
• automatically marking, by the at least one processor, the at least one device capable of processing the multi-dimensional data stream with a pre-generated label based on a determination that the modified anomaly score, from the plurality of anomaly scores and corresponding to a selected monitored segmentation of the plurality of monitored segmentations, meets or exceeds a predetermined threshold.

A system comprises:

• a non-transient computer memory, storing software instructions;
• at least one processor of a first computing device associated with a user;
• wherein, when the processor executes the software instructions, the first computing device is programmed to:
• identify at least one device capable of processing a multi-dimensional data stream;
• dynamically calculate a plurality of hash keys respectively for a plurality of monitored segmentations associated with the at least one device capable of processing the multi-dimensional data stream, the plurality of monitored segmentations generated based on at least one feature;
• generate at least one increment data counter that corresponds to each hash key of the plurality of hash keys in a plurality of counting structures,
• wherein each of the plurality of counting structures comprises,
• a current counting structure, counting the at least one incremental data counter during a current time period, and
• a total counting structure, counting the at least one incremental data counter during a total time period;
• dynamically calculate an anomaly score for each of the plurality of monitored segmentations based on at least one value of corresponding one of the plurality of counting structures to generate a plurality of anomaly scores for the plurality of monitored segmentations;
• and
• automatically mark the at least one device capable of processing the multi-dimensional data stream with a pre-generated label based on a determination that an anomaly score, from the plurality of anomaly scores and corresponding to a selected monitored segmentation of the plurality of monitored segmentations, meets or exceeds a predetermined threshold.