INFLUENCE OF ATTACK INDICATOR RANKING ON THE QUALITY OF MACHINE LEARNING MODELS IN AGENT-BASED CONTINUOUS AUTHENTICATION SYSTEMS

Security agents of authentication systems function in automatic mode and control the behavior of subjects, analyzing their dynamics using both traditional (statistical) methods and methods based on machine learning. The expansion of the cybersecurity fabric paradigm actualizes the improvement of adaptive explicable methods and machine learning models. Purpose: the purpose of the study was to assess the impact of ranking methods at compromise indicators, attacks indicators and other futures on the quality of detecting network traffic anomalies as part of the security fabric with continuous authentication. Probabilistic and explicable methods of binary classification were used, as well as nonlinear regressors based on decision trees. The results of the study showed that the methods of pre liminary ranking increase the F1-Score and functioning speed for supervised ML-models by an average of 7%. In unsupervised models, preliminary ranking does not significantly affect the training time, but increases the by 2-10%, which justifies their expediency in agent based systems of continuous authentication. Practical relevance: the models developed in the work substantiate the feasibility of mechanisms for preliminary ranking of compromise and attacks indicators, creating patterns prototypes of attack indicators in automatic mode. In general, uncontrolled models are not as accurate as controlled ones, which actualizes the improvement of either explicable uncontrolled approaches to detecting anomalies, or approaches based on methods with reinforcement.