Journal's Facebook pageAn Approach for Selecting Countermeasures against Harmful Information based on Uncertainty Management
- St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS)
39, 14th Liniya, 199178, St. Petersburg, Russia
{ivkote, ibsaen, parashchuk, doynikova}@comsec.spb.ru
Abstract
Currently, one of the big problems in the Internet is the counteraction against the spread of harmful information. The paper considers models, algorithms and a common technique for choosing measures to counter harmful information, based on an assessment of the semantic content of information objects under conditions of uncertainty. Methods of processing incomplete, contradictory and fuzzy knowledge are used. Two cases of the algorithm implementation to eliminate the uncertainties in the assessment and categorization of the semantic content of information objects are analyzed. The first case is focused on processing fuzzy data. The second case is based on using an artificial neural network. An experimental evaluation of the proposed technique have shown that the use of both cases makes it possible to eliminate uncertainties of any type and, thereby, to increase the efficiency of choosing measures to counter harmful information.
Key words
harmful information, assessment, countermeasures, semantic content, information objects, uncertainty
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS210211057K
Publication information
Volume 19, Issue 1 (January 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
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How to cite
Kotenko, I., Saenko, I., Parashchuk, I., Doynikova, E.: An Approach for Selecting Countermeasures against Harmful Information based on Uncertainty Management. Computer Science and Information Systems, Vol. 19, No. 1, 415433. (2022), https://doi.org/10.2298/CSIS210211057K
