VERIFICATION OF UNRELIABLE PARAMETERS OF THE MALICIOUS INFORMATION DETECTION MODEL-Reference-Cited by-同舟云学术

VERIFICATION OF UNRELIABLE PARAMETERS OF THE MALICIOUS INFORMATION DETECTION MODEL

Published:2019-04-25 Issue: Volume: Page:7-18
ISSN:2072-9502
Container-title:Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics
language:en
Short-container-title:

Author:

Kotenko Igor Vitalievich¹,Parashchuk Igor Borisovich¹

Affiliation:

1. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences

Abstract

The object of research is the process of detecting harmful information in the social networks and global network. There has been proposed the approach to verifying the parameters of a mathematical model of a random process of detecting malicious information with the unreliable, inaccurately (contradictory) given initial data. The approach is based on using stochastic equations of state and observation that are based on controlled Markov chains in finite differences. At the same time, verification of key parameters of a mathematical model of this type - elements of a matrix of one-step transition probabilities - is performed by using an extrapolating neural network. This allows to take into account and compensate the inaccuracy of the original data inherent in random processes of searching and detecting malicious information, as well as to increase the accuracy of decision-making on the assessment and categorization of digital network content to detect and counter information of this class.

Publisher

Astrakhan State Technical University

Reference13 articles.

1. Kotenko I. V., Saenko I. B., Chechulin A. A., Desnitsky V. A., Vitkova L. A., Pronoza A. A. Monitoring and counteraction to malicious influences in the information space of social networks // Proceedings The 10th Social Informatics conference (SocInfo2018) (Saint-Petersburg, September 25-28, 2018). Saint-Petersburg, 2018. Part II. Lecture Notes in Computer Science. V. 11186, Springer. P. 159-167., Kotenko I. V., Saenko I. B., Chechulin A. A., Desnitsky V. A., Vitkova L. A., Pronoza A. A. Monitoring and counteraction to malicious influences in the information space of social networks // Proceedings The 10th Social Informatics conference (SocInfo2018) (Saint-Petersburg, September 25-28, 2018). Saint-Petersburg, 2018. Part II. Lecture Notes in Computer Science. V. 11186, Springer. P. 159-167.

2. Kotenko I. V., Saenko I. B., Chechulin A. A. Protection against information in eSociety: using Data Mining methods to counteract unwanted and malicious data // Digital Transformation and Global Society. Second International Conference, DTGS 2017 (Saint-Petersburg, June 21-23, 2017). Revised Selected Papers. Communications in Computer and Information Science (CCIS). Saint-Petersburg, 2017. V. 745. P. 170-184., Kotenko I. V., Saenko I. B., Chechulin A. A. Protection against information in eSociety: using Data Mining methods to counteract unwanted and malicious data // Digital Transformation and Global Society. Second International Conference, DTGS 2017 (Saint-Petersburg, June 21-23, 2017). Revised Selected Papers. Communications in Computer and Information Science (CCIS). Saint-Petersburg, 2017. V. 745. P. 170-184.

3. Kosko B. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs: Prentice-Hall, 1992. 346 p., Kosko B. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs: Prentice-Hall, 1992. 346 p.

4. Kriesel D. A Brief Introduction to Neural Networks. Cambridge: Cambridge Press, 2010. 226 p., Kriesel D. A Brief Introduction to Neural Networks. Cambridge: Cambridge Press, 2010. 226 p.

5. Parlos A. G., Menon S. K., Atiya A. F. An algorithmic approach to adaptive state filtering using recurrent neural networks // IEEE Trans. Neural Networks. 2001. V. 12. N. 6. P. 1411-1432., Parlos A. G., Menon S. K., Atiya A. F. An algorithmic approach to adaptive state filtering using recurrent neural networks // IEEE Trans. Neural Networks. 2001. V. 12. N. 6. P. 1411-1432.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Features of the assessment of malicious activity in the Smart City infrastructure based on information granulation and fuzzy granular calculations;Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics;2024-07-29

2. Identification of user profiles in online social networks: a combined approach with face recognition;Journal of Physics: Conference Series;2021-05-01