The information resources of the Internet contain a lot of hidden knowledge. This knowledge is contributed by users forming a kind of expert environment. In this regard, the main task of open source intelligence technologies (OSINT) is identification and extraction of hidden expert knowledge, their generalization, as well as further analytical processing. To achieve this purpose, methods of in-depth data analysis (Text Mining), linguistic and statistical methods, as well as methods of cluster analysis are used. The paper suggests a method of extracting concepts from the texts of messages of network sources related to the subject area of cybersecurity. These concepts are filtered according to statistical characteristics and ranking. A network of their relationships is created, clustered and visualized. To create a software implementation of the suggested approaches, the Perl programming language is used in the Linux OS environment, as well as software tools for graph modeling, analysis, and visualization - Gephi.

Keywords

OSINT, cybersecurity objects, time series, concept extraction, terms network, web resources