Machine Learning for Cybersecurity Enhancing Threat Detection Systems

Abstract

The rise of cyber threats has highlighted the need for advanced methods in cybersecurity. Machine learning (ML) has emerged as a powerful tool to enhance threat detection systems by enabling the automated identification of complex patterns and anomalies. This paper explores the role of ML in cybersecurity, focusing on its applications in threat detection, risk assessment, and intrusion detection systems. Key ML techniques, such as supervised learning, unsupervised learning, and reinforcement learning, are evaluated for their effectiveness in identifying security threats. Challenges and future directions for ML-driven cybersecurity solutions are also discussed, providing insights into the ongoing evolution of cybersecurity practices.