Year: 2024
Type: article
Abstract: Using machine learning (ML) has made it faster and more precise to discover cyber security threats. Older methods of detecting threats usually struggle with today’s attack volume and complexity which causes delays and can result in mistakes. ML technology helps security teams notice known and new threats in a much shorter period than manual detection. Adopting supervised and unsupervised model types, they can adapt to any new kinds of attacks, raising the chance of detecting them with fewer errors. This study assesses different ML tools and explores when they show better outcomes than standard security systems. The analysis shows that including ML in cyber defense plans increases how well we detect threats, responds to security incidents and safeguards the organization. The findings recommend that companies rely heavily on smart and automated tools for threat detection in cyber security.
Authors: Kailash Dhakal, Mohammad Mosiur Rahman, Mashfiquer Rahman, Khairul Anam, Mostafizur Rahman, Ramesh Poudel
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411609270