MACHINE LEARNING AND DEEP LEARNING FOR CYBER SECURITY

Abstract

In today's interconnected digital landscape, the rapid evolution of cyber threats poses significant challenges to network security infrastructure. This research paper examines the application of machine learning (ML) and deep learning (DL) methodologies in enhancing cybersecurity frameworks, particularly focusing on intrusion detection systems. We present a comprehensive analysis of various ML and DL algorithms, their implementation strategies, and their effectiveness in identifying and mitigating cyber threats. The study emphasizes the importance of robust datasets in training effective security models and addresses the current limitations and future prospects of AI-driven cybersecurity solutions.