Cybersecurity Threat Detection Using Machine Learning in Cloud-Based Environments: A Comprehensive Study

Abstract

The use of cloud computing has made cyb?rs?curity a top priority. Traditional s?curity m?asur?s in dynamic cloud syst?ms rar?ly d?t?ct ?m?rging thr?ats and pr?v?nt th?m from taking action. Th? us? of machin? l?arning algorithms to id?ntify cyb?rs?curity risks in cloud bas?d ?nvironm?nts has b??n ?xplor?d in this ?xt?nsiv? r?vi?w. To configur? risks such as malwar? inf?ctions and p?rsist?nt advanc?d thr?ats and unauthoriz?d acc?ss att?mpts and d?nial of s?rvic? attacks and an int?gration strat?gy that ?.g. mor? vari?ty looks at this sup?rvis?d and unsup?rvis?d and ?ff?ctiv? group l?arning m?thod. Various adv?rsary training t?chniqu?s w?r? us?d to improv? th? r?sili?nc? of th? mod?l to hostil? attacks. This work addresses issues such as data acc?ssibility and mod?l int?rpr?tation and th? dynamic natur? of cyb?r thr?ats and d?monstrat?s th? ?ff?ctiv?n?ss of machin? l?arning in d?t?cting sophisticat?d attacks. It op?ns th? door for s?curity improv?m?nts.