Tools of the Neuro-Fuzzy Model of Information Risk Management in National Security
Alla Khomutenko1, Alla Mishchenko2, Artem Ripenko3, Olha Frum4, Zoreslava Liulchak5, Roman Hrozovskyi6

1Alla Khomutenko*, Finance Department of Odessa National Economic University, Odessa, Ukraine
2Alla Mishchenko, Department of International Relations, Faculty of Journalism and International Relations, Kyiv National University of Culture and Arts, Kyiv, Ukraine
3Artem Ripenko, Odessa Forensic Research Institute of Ministry of Justice of Ukraine, Odessa, Ukraine
4Olha Frum, Department of Industrial Economics, Odessa National Academy of Food Technologies, Odessa, Ukraine
5Zoreslava Liulchak, Department of Marketing and Logistics Lviv Polytechnic National University, Lviv, Ukraine
6Roman Hrozovskyi, Research Laboratory of the Department of Application of Information Technologies and Information Fight of the National Defence University of Ukraine named after Ivan Cherniakhovskyi, Kyiv, Ukraine
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 4526-4530 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8842088619/2019©BEIESP | DOI: 10.35940/ijeat.F8842.088619
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Abstract: The rapid development of information technology has strengthened the importance of the information risk management system. Integrated systems for storing and processing information, its transmission channels, as well as the information itself, are strategically essential objects of national security. The growing volumes of statistical data, as well as the traditional uncertainty and incompleteness of information on the nature of potential threats, determine the need to use new approaches for risk analysis. The neuro-fuzzy model considered in the article is based on the advantages of fuzzy logic and artificial neural networks. The proposed neuro-fuzzy network is adapted for continuous risk analysis and iterative implementation of the analysis stage. It eliminates the disadvantages of the fuzzy logical model and takes full advantage of neural networks. This system copes well with large volumes of information since there is a direct correlation between the amount of data and the speed of network learning. The data provided by the network at the output is expressed in understandable terms and sufficient to make a balanced and reasoned decision on information risk management.
Keywords: Neuro-Fuzzy Model, Risk, Information Risks, Risk Management, National Security.