
Ushbu maqolada kiberxavfsizlik sohasida sun’iy intellekt asosida tahdidlarni aniqlash algoritmlari batafsil tahlil qilinadi. Zamonaviy axborot tizimlarida kiberhujumlar tobora murakkablashib borayotganligi sababli, an’anaviy xavfsizlik vositalari yetarli samaradorlik ko‘rsata olmayapti. Shu bois, maqolada mashina o‘rganish va chuqur o‘rganish yondashuvlari asosida tahdidlarni aniqlash usullari ko‘rib chiqilgan. Tarmoqdagi anomaliyalarni aniqlash, zararli dasturiy ta’minot va kiberhujumlarni ilgari surish imkonini beruvchi sun’iy intellekt algoritmlarining afzalliklari va kamchiliklari tahlil qilingan
Kiberxavfsizlik, Sun'iy intellekt, Tahdidlarni aniqlash, Mashina o'rganish, Chuqur o'rganish, Anomaliya aniqlash, Neyron tarmoqlar, XGBoost, Gibrid algoritmlar, Tarmoq xavfsizligi.
Kiberxavfsizlik, Sun'iy intellekt, Tahdidlarni aniqlash, Mashina o'rganish, Chuqur o'rganish, Anomaliya aniqlash, Neyron tarmoqlar, XGBoost, Gibrid algoritmlar, Tarmoq xavfsizligi.
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
