Formal security models
https://doi.org/10.22405/2226-8383-2021-22-1-488-494
Abstract
In paper describes an approach to building a formal model of information security based on the use of predicate algebra. The model is represented as a decision tree. The algorithm of its construction based on the deductive method of searching for answers is developed and investigated.
About the Author
Vyacheslav Leonidovich Tokarev
Tula State University
Russian Federation
Russian Federation
doctor of technical sciences, professor
References
1. “ISO/1EC 15408-1 2009 , Evaluation criteria for IT security, Part 1: Introduction and general model”.
2. Devyanin P. N. 2008, “On the development of security models for information flows in computer systems with role-based access control”, Materials of the 3rd international scientific conference on security and counteraction to terrorism. Lomonosov Moscow state University. 25-27 October
3. ,Moscow: ICNMO,. - pp. 261-265.
4. Devyanin P. N. 2012, “The Problem of justification of adequacy of formal security models of logical access control and their implementation in computer systems”, High availability systems, no. 2, pp. 45-49.
5. Olifer V. G.& Olifer N. A. 2011, “Computer networks. Principles, technologies, and protocols” , St. Petersburg: Piter, 944 p.
6. Quinlan, J. R. 1986, “Induction of Decision Trees. Machine Learning 1”, Kluwer Academic Publishers, pp. 81-106.
7. Kashnitsky, Y. S. 2016, “Methods of the search of accurate and interpretable rules for classifying data with complex structure” ,XV artificial intelligence conference КИИ-2016 (Smolensk, Russia), Proceedings of the conference. In 3-t., Smolensk, Publishing House «Универсум», pр.183-191.
8. Quinlan, J. R. 1990, “Probabilistic decision trees”. In: Y. Kodratoff and R. Michalski (Eds.), “Machine Learning. An Artificial Intelligence Approach”. Vol. III, Morgan Kaufmann Publishers, Inc., pp. 140–152
9. Jerzy, W. GRZYMALA-BUSSE, 1993, "Selected Algorithms of Machine Learning from Examples". Department of Computer Science, University of Kansas, KS 66045, USA
10. Taggart A.J, DeSimone A.M, Shih J.S, Filloux M.E and Fairbrother W.G. 2012, "Large-scale
11. mapping of branchpoints in human pre-mRNA Nat Struct Mol Biol. PMID: 22705790; PMCID: PMC3465671. pp.719–721.
12. Quinlan J. R. 1992, “Learning With Continuous Classes”, Proceedings of the 5th Australian Joint Conference on Artificial Intelligence. pp. 343—348.
13. J.R. Quinlan 1993, “Programs for Machine Learning”. Morgan Kaufmann, San Mateo, California.
14. Quinlan J.R. 1996, “Improved Use of Continuous Attributes in C4.5”, Journal of Artificial Intelligence Research. Vol. 4, pp. 77—90.
15. Breiman L., Friedman J.H. , Olshen R.A. and Stone C.T. 1984, “Classification and Regression Trees”. Wadsworth, Belmont, California.
16. Michie D., Spiegelhalter D.J., Taylor C.C. 1994, “Machine Learning, Neural and Statistical Classification”, 02,17, Journal of the American Statistical Association.
17. Marzinotto, Alejandro; Colledanchise, Michele; Smith, Christian; ¨ Ogren, Petter 2014, "Towards a Unified BTs Framework for Robot Control". Robotics and Automation (ICRA), IEEE International Conference.
18. Colledanchise, Michele; ¨ Ogren, Petter, Michele, Colledanchise and Petter, Ogren 2018, ‘Behavior Trees in Robotics and AI”. CRC Press. arXiv:1709.00084.3 Jun 2020.
19. ¨ Ogren, Petter 2012, "Increasing Modularity of UAV Control Systems using Computer Game Behavior Trees"(PDF). AIAA Guidance, Navigation and Control Conference, Minneapolis,
20. Minnesota. pp. 13–16.
21. Kl¨ockner, Andreas 2013, "Behavior Trees for UAV Mission Management". GI-Jahrestagung. pp. 57–68.
Review
For citations:
Tokarev V.L. Formal security models. Chebyshevskii Sbornik. 2021;22(1):488-494. (In Russ.) https://doi.org/10.22405/2226-8383-2021-22-1-488-494
Views:
334
Email this article 