Segmenting Input Data to Improve the Quality of Identification of Information Security Events

Abstract

The processing of information sequences using segmentation of input data, aimed at improving the quality indicators of destructive impact detection using machine learning models is proposed. The basis of the proposed solution is the division of data into segments with different properties of the objects of observation. A method is described that uses a multilevel data processing architecture, where the processes of training, analysis of the achieved values of quality indicators, and assignment of the best models for quality indicators to individual data segments are implemented at various levels. The proposed method allows us to improve the quality indicators of the detection of destructive information impacts through segmentation and assignment of models that have the best indicators in individual segments.