Ключевые слова
Аннотация
Ushbu maqolada kiber tahdidlarni aniqlashda mashinaviy o‘rganish texnologiyalarining roli yoritilgan. Bugungi kunda kiber xavfsizlik sohasida tahdidlarning murakkablashuvi va sonining ortib borishi samarali aniqlash va qarshi kurashish usullariga bo‘lgan ehtiyojni oshirmoqda. Maqolada nazorat ostidagi va nazoratsiz o‘rganish usullari, shuningdek chuqur o‘rganish (deep learning) algoritmlarining samaradorligi tahlil qilinadi. Eksperimentlar mashinaviy o‘rganish asosidagi yondashuvlar yordamida kiber hujumlarni yuqori aniqlikda aniqlash imkonini berishini ko‘rsatdi. Shuningdek, bu texnologiyalarning afzalliklari, cheklovlari va kelajakdagi rivojlanish yo‘nalishlari muhokama qilinadi.
Библиографические ссылки
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.– Mashinaviy o‘rganishning nazariy asoslari.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.– Chuqur o‘rganish bo‘yicha fundamental darslik.
Zhang, Y., & Chia, Y. K. (2020). Machine Learning for Cybersecurity. Springer.– Aynan kiberxavfsizlikda ML texnikalarining qo‘llanishi haqida.
Kshetri, N. (2019). The Emerging Role of Big Data and Analytics in Cybersecurity.– Big Data va ML kiber tahdidlarni aniqlashdagi roli.
Ilmiy maqolalar va konferensiyalar:
Sommer, R., & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. IEEE Symposium on Security and Privacy. IDS uchun mashinaviy o‘rganishga tanqidiy yondashuv.
Dhanabal, L., & Shantharajah, S. P. (2015). A study on NSL-KDD dataset for intrusion detection system based on classification algorithms.– IDS bo‘yicha ML algoritmlarini solishtirish.
Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016). A Deep Learning Approach for Network Intrusion Detection System. Proceedings of the 9th EAI International Conference.
– Deep learning asosida IDS tizimi yaratish.
Ullah, I., & Mahmoud, Q. H. (2019). A Hybrid Intrusion Detection System Based on Deep Learning. IEEE Access.– Chuqur o‘rganishga asoslangan hibrid IDS tizimi.
Onlayn manbalar (open-access):
Kaggle – NSL-KDD Dataset https://www.kaggle.com/datasets/defcom17 /nslkdd - IDS modellari uchun ommaviy ochiq ma’lumotlar to‘plami.
MIT Lincoln Laboratory – Cybersecurity Dataset Repository https://www.ll.mit.edu/r-d/datasets - Real tarmoq trafiklariga oid kiberxavfsizlik ma’lumotlar bazasi.
Scikit-learn Documentation https://scikit-learn.org - Mashinaviy o‘rganish algoritmlarining amaliy qo‘llanilishi.
TensorFlow & Keras Tutorials – Cybersecurity Applications https://www.tensorflow.org/tutorials - ML va DL modellarni real tahdidlar ustida qurish bo‘yicha yo‘riqnomalar.
Qo‘shimcha foydali adabiyotlar:
Sarker, I. H. (2022). Cybersecurity data science: An overview from machine learning perspective. Journal of Big Data.– Kiberxavfsizlikda ML qo‘llanilishining keng tahlili.
Chio, C., & Freeman, D. (2018). Machine Learning and Security: Protecting Systems with Data and Algorithms. O’Reilly Media.– Real tahdidlar va ularni aniqlashda ML yondashuvlari.