Abstract
The research object is the process of detecting harmful information in social networks and the global network. The chapter proposes an approach for determining (verifying) the parameters of a mathematical model of a stochastic process for detecting harmful information with unreliable, inaccurate (contradictory) specified initial data. The approach is based on the use of stochastic equations of state and observation based on controlled Markov chains with finite differences. Moreover, the determination (verification) of key parameters of the mathematical model of this type (the elements of the matrix of one-step transition probabilities) is carried out by using an extrapolating neural network. This allows one to take into account and compensate for the inaccuracy of the original data inherent stochastic processes of searching and detecting harmful information. In addition, this approach allows one to increase the reliability of decision-making in evaluating and categorizing the digital network content for detecting and counteracting information of this class.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elahi, G., Yu, E., Zannone, N.: A modeling ontology for integrating vulnerabilities into security requirements conceptual foundations. In: Proceedings of ER-2009, pp. 99–114. Springer (2009)
Kotenko, I., Saenko, I., Chechulin, A., Desnitsky, V., Vitkova, L., Pronoza, A.: Monitoring and counteraction to malicious influences in the information space of social networks. In: Proceedings of 10th Social Informatics Conference (SocInfo2018), September 25–28, 2018, Saint Petersburg, Russia. Part II. Lecture Notes in Computer Science, vol. 11186, pp. 159–167. Springer (2018)
Kotenko, I., Saenko, I., Chechulin, A.: Protection against information in eSociety: using Data Mining methods to counteract unwanted and malicious data. In: Proc. of Digital Transformation and Global Society. In: Second International Conference DTGS. Communications in Computer and Information Science (CCIS), vol. 745, pp. 170–184 (2017)
Kosko, B.: Neural Networks and Fuzzy Systems. A Dynamical Systems Approach to Machine Intelligence. Prentice-Hall, Englewood Cliffs (1992)
Kriesel, D.: A Brief Introduction to Neural Networks. Cambridge Press, Cambridge (2010)
Parlos, A., Menon, S., Atiya, A.: An algorithmic approach to adaptive state filtering using recurrent neural networks. IEEE Trans. Neural Netw. 12(6), 1411–1432 (2001)
Rojas, R.: Neural Networks. Springer, Berlin (1996)
Muller, B., Reinhardt, J., Strickland, M.: Neural Networks: An Introduction. Springer (1995)
Anderson, J., Rosenfeld, E.: Neurocomputing: Foundation of Research. MIT Press, Cambridge (1988)
Nesteruk, G., Kupriyanov, M.: Neural-fuzzy systems with fuzzy links. In: Proceedings of the VI-th International Conference SCM’2003. St.Pb, StPSETU «LETI», vol. 1, pp. 341–344 (2003)
Kotenko, I., Parashchuk, I., Omar, T.: Neuro-fuzzy models in tasks of intelligent data processing for detection and counteraction of inappropriate, dubious and harmful information. In: Proceedings of the 2nd International Scientific-Practical Conference Fuzzy Technologies in the Industry (FTI-2018). CEUR Workshop Proceedings (CEUR-WS), vol. 2258, pp. 116–125 (2018)
Parashchuk, I.: System formation algorithm of communication network quality factors using artificial neural networks. In: Proceedings of 1st IEEE International Conference on Circuits and System for Communications (ICCSC’02), St.Pb, SPbGTU, pp. 263–266 (2002)
Stewart, N., Thomas, G.: Markov processes. In: Proceedings of Probability and Mathematical Statistics, pp. 214–234. Wiley, New York (1986)
Bini, D., Latouche, G., Meini, B.: Numerical Methods for Structured Markov Chains. Oxford University Press, New York (2005)
Yuksel, S.: Control of Stochastic Systems. Queen’s University Mathematics and Engineering and Mathematics and Statistics (2017)
Van Handel, R.: Stochastic Calculus, Filtering and Stochastic Control. Springer (2007)
Dobre, T.: In: T.G. Dobre, J.G. Sanchez Marcano (eds.) Chemical Engineering: Modeling, Simulation and Similitude. Wiley, Weinheim (2007)
Oliver, D., Kelliher, T., Keegan, J.: Engineering Complex Systems With Models and Objects. McGraw-Hill, New York (2007)
Oksendal, B.: Stochastic Differential Equations. An Introduction with Applications. Springer, Berlin (2007)
Iacus, S.: Simulation and Inference for Stochastic Differential Equations. With R Examples. Springer, New York (2008)
Quarteroni, A.: Mathematical Models in Science and Engineering. Proc. Not. AMS 56(1), 9–19 (2009)
Acknowledgements
The work is performed by the grant of RSF #18-11-00302 in SPIIRAS.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kotenko, I., Parashchuk, I. (2020). Determining the Parameters of the Mathematical Model of the Process of Searching for Harmful Information. In: Kravets, A., Bolshakov, A., Shcherbakov, M. (eds) Cyber-Physical Systems: Industry 4.0 Challenges. Studies in Systems, Decision and Control, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-030-32648-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-32648-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32647-0
Online ISBN: 978-3-030-32648-7
eBook Packages: EngineeringEngineering (R0)