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.
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The work is performed by the grant of RSF #18-11-00302 in SPIIRAS.
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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
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DOI: https://doi.org/10.1007/978-3-030-32648-7_18
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