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Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 2021, Volume 61, Number 7, Pages 1125–1136
DOI: https://doi.org/10.31857/S004446692107005X (Mi zvmmf11263) 		 
Computer science

Dynamic Bayesian networks as a testing tool for fuzzing web applications

T. V. Azarnova, P. V. Polukhin

Voronezh State University, 394018, Voronezh, Russia
DOI: https://doi.org/10.31857/S004446692107005X
Abstract: Simulation of testing web applications using fuzzing and dynamic Bayesian networks is considered. The basic principles of optimizing the structure of dynamic Bayesian networks are formulated, and hybrid algorithms for learning and probabilistic inference using quasi-Newtonian algorithms and elements of the theory of sufficient statistics are proposed.
Key words: dynamic Bayesian networks, Markov process, Schwarz criterion, probabilistic inference, particle filter, conditional independence criterion, Rao–Blackwell–Kolmogorov theorem, Levenberg–Marquardt algorithm, Broyden's method.
Received: 26.11.2020
Revised: 26.11.2020
Accepted: 11.03.2021
English version:
Computational Mathematics and Mathematical Physics, 2021, Volume 61, Issue 7, Pages 1118–1128
DOI: https://doi.org/10.1134/S0965542521070058
Bibliographic databases:
Document Type: Article
UDC: 519.72
Language: Russian
Citation: T. V. Azarnova, P. V. Polukhin, “Dynamic Bayesian networks as a testing tool for fuzzing web applications”, Zh. Vychisl. Mat. Mat. Fiz., 61:7 (2021), 1125–1136; Comput. Math. Math. Phys., 61:7 (2021), 1118–1128
Citation in format AMSBIB
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\paper Dynamic Bayesian networks as a testing tool for fuzzing web applications
\jour Zh. Vychisl. Mat. Mat. Fiz.
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\vol 61
\issue 7
\pages 1125--1136
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\jour Comput. Math. Math. Phys.
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    		Журнал вычислительной математики и математической физики Computational Mathematics and Mathematical Physics
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