SUN’IY INTELLEKT ASOSIDA AQLLI AGENTLAR YORDAMIDA KIBERHUJUMLARNI ANIQLASH VA OLDINI OLISH USULLARI
Kalit so'zlar
https://doi.org/10.47390/nat-i3v2y2026/n04Kalit so'zlar
sun’iy intellect. aqlli agentlar, kiberxavfsizlik, kiberhujumlar, IDS, IPS, mashinali o‘qitish, chuqur o‘rganish, anomaliyani aniqlash, tarmoq xavfsizligi.Annotasiya
Mazkur maqolada sun’iy intellekt texnologiyalari va aqlli agentlardan foydalangan holda kiberhujumlarni aniqlash hamda ularning oldini olish usullari ilmiy nuqtai nazardan tahlil qilinadi. Raqamli infratuzilmalar rivojlanishi bilan bir qatorda kiberxavfsizlikka tahdidlar ham murakkablashib borayotgani sababli an’anaviy qoidalarga asoslangan himoya vositalarining imkoniyatlari cheklanmoqda. Tadqiqotda mashinali o‘qitish, chuqur o‘rganish va anomaliyalarni aniqlash algoritmlaridan foydalanuvchi aqlli agentlar konsepsiyasi ko‘rib chiqiladi. Shuningdek, tarmoq monitoringi, trafikni tahlil qilish, hujumlarni tasniflash va avtomatik javob berish jarayonlari yagona adaptiv tizim doirasida yoritiladi. Taklif etilgan modelning afzalliklari aniqlik, moslashuvchanlik va real vaqt rejimida ishlash imkoniyatlari bilan izohlanadi. Tadqiqot natijalari SI asosidagi aqlli agentlar kelajakdagi kiberxavfsizlik infratuzilmalarining muhim tarkibiy qismiga aylanishi mumkinligini ko‘rsatadi.
Manbalar
1. Bizzarri, A., Yu, C.-E., Jalaian, B., Riguzzi, F., & Bastian, N. D. (2025). Neurosymbolic AI for network intrusion detection systems: A survey. Journal of Information Security and Applications, 94, 104205.
2. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.
3. Mohale, V. Z., & Obagbuwa, I. C. (2025). A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhance transparency and interpretability in cybersecurity. Frontiers in Artificial Intelligence, 8, Article 1526221.
4. Pawlicki, M., Pawlicka, A., Kozik, R., et al. (2024). The survey on the dual nature of explainable AI challenges in intrusion detection and their potential for AI innovation. Artificial Intelligence Review, 57, Article 330.
5. Salem, A. H., Azzam, S. M., Emam, O. E., & Abohany, A. (2024). Advancing cybersecurity: A comprehensive review of AI-driven detection techniques. Journal of Big Data, 11, Article 105.
6. Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41–50.
7. Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., & Venkatraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7, 41525–41550.
8. Wang, W., Sheng, Y., Wang, J., Zeng, X., Ye, X., Huang, Y., & Zhu, M. (2018). HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access, 6, 1792–1806.
9. Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954–21961.
10. Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016). A deep learning approach for network intrusion detection system. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (pp. 21–26).
11. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
12. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
13. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
14. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
15. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.