Abstract.A new method of text steganography based on Markov chains of different orders that allows the introduction of hidden information in texts is presented together with test results of a software solution which generate texts with a good approximation to the natural language model. Keywords: text steganography, Markov process, automatic text generation, text naturalness. INTRODUCTIONSteganography is a science of a hidden data transmission by the concealment of the fact of data transfer. Currently, the problem of steganographic data protection from unauthorized access is extremely urgent [1]. However, most studies in this area are focused either on a concept of hidden information embedded in multimedia containers (images, audio and video files) of different formats, or aimed at the use of telecommunication networks (network steganography). At the same time, the development of linguistic steganography, which uses text information as a container, has received too little attention. This is explained by the fact that the steganographic methods based on the embedding of hidden information in media files, as well as in telecommunication networks, in fact, are not suitable when using natural language texts as a steganographic container. Nevertheless, methods of text steganography can be widely used, since, according to statistics, it is textual information that has the highest transmission intensity [2]. The advantage of text containers over other media containers lies in the fact that methods of analysis of text files for the presence of hidden information have not been fully implemented at present [3]. At the same time, there are many algorithms designed exclusively for text containers, a description of which can be found, for example, in [4,5]. Currently, there are a lot of methods of text steganography based on the use of Markov models. For example, in [2, 6] input data is used for text generation using Markov chains. However, the proposed models are greatly simplified in order to facilitate calculation, since it is assumed that all probabilities of transition from a given state to any other are equal. The submitted paper is based on the studies of [7], in which the probabilities of transition of one word to another are maintained with sufficiently fair accuracy. The novelty of the method presented in this paper is that the proposed steganographic method is based on higher-order Markov processes (second and third) and also allows for working with Russian texts.