Intelligent Threat Detection: Leveraging Machine Learning for Cybersecurity Automation

Abstract

As the digital landscape grows increasingly complex, traditional cybersecurity systems are struggling to keep pace with evolving threats, sophisticated malware, and large-scale data breaches. Machine learning (ML) has emerged as a pivotal technology in modern cybersecurity, providing automation, adaptability, and predictive intelligence for detecting and mitigating cyber threats. This paper explores how ML techniques are revolutionizing cybersecurity by enabling real-time anomaly detection, intelligent intrusion prevention, and automated incident response. The discussion encompasses both supervised and unsupervised approaches for threat identification, the integration of ML with security orchestration, and the challenges of data imbalance, adversarial attacks, and explainability. By examining current advancements and future trends, this paper underscores how ML-driven cybersecurity automation enhances detection accuracy, response efficiency, and resilience against emerging digital threats.