MASKASÍZ QÁSIYETLERDI ANÍQLAW ARQALÍ INFORMACIYA QÁWIPSIZLIGIN TÁMIYINLEW USHÍN NEYRO-ANÍQ EMES MODELLESTIRIW - Dosimbetov, A.M.; Turdishov, D.X.; Mambetnazarova, A.Q. (ILIM hám JÁMIYET, 2025)
The presented neuro-fuzzy model for detecting unmasked properties in web applications represents a hybrid system that combines fuzzy logic modeling capabilities with adaptive learning capabilities of neural networks. This model solves the problem of detecting properties that intentionally reveal secure information about the protected object, such as web application software stacks or cryptographic vulnerabilities that can be used in cyberattacks. Based on the framework proposed by Korochentev and Pavlenko, the model is built in the form of a seven-story architecture that processes input properties, changes them through fuzzy inference, and generates an integral risk score. This article examines the model structure, its layers, mathematical foundations, and adaptations for practical analysis of web application security.