Devising a method for detecting information threats in the Ukrainian cyber space based on machine learning

The object of this study is a disinformation detection process based on search algorithms for identifying fake news. The main task was to define a set of criteria and parameters for detecting the Ukrainian-language disinformation based on machine learning. A methodology has been considered for developing and filling a dataset of fakes for further training of the model and testing it for the purpose of identifying disinformation and propaganda, as well as determining the attributes of primary sources and routes of their distribution. This makes it possible to reasonably approach the definition of a model for forecasting the development of information threats in the cyberspace of Ukraine. In particular, the accuracy of automatic detection of the probability of disinformation in texts can be increased. For the English-language texts using balanced datasets for training when applying classical machine learning classifiers, the accuracy of identification and recognition of fakes is ³90 %, and for the Ukrainian-language texts – ³52 % and £90 %. That has made it possible to devise requirements for the structure and content of a typical dataset of fakes in the period after the full-scale invasion of Ukraine. The practical result of this work is the designed decision-making support system for monitoring, detecting, recognizing, and forecasting information threats in the cyberspace of Ukraine based on NLP and machine learning. The implementation of preliminary processing of the Ukrainian-language news, taking into account the linguistic features of the language in the text, increases the accuracy of fake identification by »1.72 times. Approaches to the construction of models for forecasting the development of information threats in cyberspace have been developed, which is an urgent task when fake news and information manipulation can affect public sentiment, politics, and the economy