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The article is devoted to the application of dynamic Bayesian networks models for fuzzing web applications and development of effective hybrid algorithms for probabilistic inference based on particle filter algorithm. Dynamic Bayesian networks models allow to simulate the dynamic process transformation of web applications associated with the process of their constant instrumental and logical updates, and create a probabilistic structure required for learning process of testing the top web applications vulnerabilities, that able to use the evidence and inference results obtained in the retrospective and current testing slices and improve testing mechanisms in new time slices. The hybrid probabilistic inference algorithm for dynamic Bayesian networks models for testing web-applications, proposed in the current research, significantly increase the efficiency of the classical approximate probabilistic inference algorithms, well reflect the features of the temporary testing links formation and adapted to the detection of anomalous errors.

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The article covers clustering algorithms for dynamic Bayesian networks based on the construction of join tree. Investigated problems associated with simplifying the topology of dynamic Bayesian networks using clustering methods and consider various semantic approaches of junction trees construction. The paper presents a junction tree constructing algorithm for the dynamic Bayesian network that takes into account the transition and perception models applied to the process of network formation. Analyzed The relationship between the complete joint probability distribution for the dynamic Bayesian network and the complete joint distribution obtained for the junction tree. It is shown that the required complete joint distribution of the junction tree for the dynamic Bayesian network can be obtained as a product of local probability distributions for each node associated with the resulting junction tree, and proved that this distribution will be equivalent to the probability distribution of the original dynamic Bayesian network. Introduced the main structural approaches to the construction of junction tree for the discrete dynamic Bayesian network and inspected the use of junction tree for solving similar problems for continuous dynamic Bayesian networks with Gaussian and exponential distribution functions of network variables.