Maskasız qásiyetlerdi anıqlaw arqalı informaciya qáwipsizligin támiyinlew ushın neyro-anıq emes modellestiriw

Mualliflar

  • A.M Dosimbetov

    Innovacion texnologiyalar universiteti

  • D.X Turdishov

    Nukus State Pedagogical Institute named after Ajiniyaz image/svg+xml

  • A.Q Mambetnazarova

    Nukus davlat texnika universiteti

Kalit so‘zlar: Web-ilova, niqobsiz xususiyatlar, himoyalanuvchi obyekt, model, Neyro-noaniq, funksiya, konfiguratsiya

Annotatsiya

Web-ilovalarda niqobsiz xususiyatlarni aniqlash uchun taqdim etilgan neyro-noaniq model noaniq mantiqni modellashtirish imkoniyatini neyron tarmoqlarining adaptiv o‘rganish imkoniyatlari bilan birlashtirgan gibrid tizimdir. Ushbu model kiberhujumlarda foydalanilishi mumkin bo‘lgan veb-ilovaning dasturiy ta’minoti steklari yoki kriptografik kamchiliklar kabi himoyalanuvchi obyekt haqidagi xavfsiz ma’lumotlarni ataylab oshkor qilib qo‘yuvchi xususiyatlarni aniqlash muammosini hal qiladi. Korochentev va Pavlenko tomonidan taklif etilgan freymvorkga tayanib, model kirish xususiyatlarini qayta ishlaydigan, ularni noaniq xulosa orqali o‘zgartiradigan va xavf-xatarning integral ballini ishlab chiqaradigan yetti qavatli arxitektura ko‘rinishida tuzilgan. Ushbu maqolada modelning tuzilishi, uning qatlamlari, matematik asoslari va web-ilovalar xavfsizligini amaliy tahlil qilish uchun moslashuvlari haqida so‘z yuritiladi.

Foydalanilgan adabiyotlar

1. Jang, J.S.R. ANFIS: Adaptive-Network-Based Fuzzy Inference System. // IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 1993. – P. 665-685.

2. Lin, C.T., & Lee, C.S. Neural Network Based Fuzzy Logic Control and Decision System. // IEEE Transactions on Computers, 40(12), 1991. – P. 1320-1336.

3. Nauck, D., Klawonn, F., Kruse, R. Foundations of Neuro-Fuzzy Systems. John Wiley & Sons. 1997.

4. Короченцев, Д.А., Павленко, А.С. Разработка нейро-нечеткой модели обеспечения информационной безопасности за счет выявления демаскирующих признаков объекта защиты. Автоматизация процессов управления, 1(59), 2020. – C. 38-46.

5. Tano S., Oyama T., Arnould T. Deep Combination of Fuzzy Inference and Neural Network in Fuzzy Inference. // Fuzzy Sets and Systems, 82(2), 1996. – P. 151-160.

6. Zadeh, L.A. Fuzzy Sets. // Information and Control, 8(3), 1965. – P. 338-353.

7. Kosko, B. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice Hall. 1992.

8. Seitnazarov K.K., Dosımbetov A.M., Aytanov A.K. Strategy for Organization of Computational Experiments of the Functioning of Underground Water Inlets Using a Fuzzy Multiple Approach. International Conference on Information Science and Communications Technologies (ICISCT). – Tashkent, Uzbekistan, 2020. – C. 1-4.