Cyberattack intensity forecasting on informatization objects of critical infrastructures

Abstract

In regulatory documents of recent years in the field of information security, much attention is paid to information systems of critical infrastructures. This, in turn, justifies the need for scientific research on the development of new methods of protection against cyberattacks on such information systems. For this task, interval forecasting is recommended based on a probabilistic neural network with dynamic updating of the smoothing parameter. As benchmarks for comparing the interval forecasting results, the naive Bayesian model and the probabilistic cluster model were chosen.