REAL-TIME FALSE INFORMATION DETECTION PREDICTION ACCURACY MODEL

Abstract

State information security is an urgent task for a country conducting military operations. Ensuring the information security of the State is an urgent scientific task for all countries. The problem of information struggle, information war has always occupied a primary place in the general security of the State. With the help of criminal informational influence, it is possible to control society, public mood. Therefore, the development and improvement of the scientific and methodological apparatus, namely the development of a forecast accuracy model for the detection of false information in real time, as a means of introducing information warfare, is an urgent scientific task. This scientific work is devoted to the solution of this scientific task. The paper examines a mathematical apparatus for detecting and blocking false information in real time. The article is devoted to solving the problem of detecting false information. The situation is considered, when the reconstructed signal is generated by the Mandelbrot model, further using the Kohonen self-learning method. Specifying the unknown parameters using the standard Utadrow-Hoff method obtained a model of the accuracy of the prediction of false information detection. Which allows you to detect false information in real time. The direction of further research can be the task of optimizing the criteria for assessing the accuracy of the forecast.