Keywords: web application vulnerabilities, bayesian network, probabilistic inference problems, testing process, monte Carlo method using Markov circuits, particle filtering algorithm
Development of a concept and tools for modeling web application testing processes using fuzzing using dynamic Bayesian networks
UDC 519.85
DOI: 10.26102/2310-6018/2023.43.4.031
Ensuring the sustainability of web applications with respect to various security threats plays a crucial role in the development of modern information support technologies for industrial enterprises, financial structures and service organizations. This explains the high relevance of the development of new scientifically sound effective computational methods, algorithms and problem-oriented programs for testing web applications with a complex functional structure of internal and external interaction, which implement the capabilities of streaming data generated from the results of each of the test steps, and the application of the results in the process of managing the testing of web applications. The article describes the concept of modeling testing processes, research of the obtained models and development of analysis and prediction algorithms, based on a formalized apparatus of dynamic Bayesian networks. The Bayesian models proposed in the paper, built on the basis of statistical training, help to determine time relationships for each of the parameters determined during the test procedure, provide the opportunity to predict test results by performing simulations using probabilistic inference methods.
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Keywords: web application vulnerabilities, bayesian network, probabilistic inference problems, testing process, monte Carlo method using Markov circuits, particle filtering algorithm
For citation: Azarnova T.V., Polukhin P.V. Development of a concept and tools for modeling web application testing processes using fuzzing using dynamic Bayesian networks. Modeling, Optimization and Information Technology. 2023;11(4). URL: https://moitvivt.ru/ru/journal/pdf?id=1479 DOI: 10.26102/2310-6018/2023.43.4.031 (In Russ).
Received 24.11.2023
Revised 11.12.2023
Accepted 22.12.2023
Published 31.12.2023