Dynamic Bayesian networks as a testing tool for fuzzing web applications

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.