Machine Learning in Cybersecurity: A Comprehensive Analysis of Intrusion Detection Systems

Abstract

Cyber threats are getting more complex and larger, and traditional security measures can't keep up with the sophisticated attacks. Machine Learning (ML) has emerged as a powerful tool in enhancing cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). a comprehensive analysis of the role of machine learning in intrusion detection, focusing on its ability to detect and respond to both known and unknown threats in real-time. various machine learning techniques, including supervised, unsupervised, and deep learning models, and evaluates their effectiveness in identifying anomalies and preventing security breaches. Key performance metrics such as accuracy, false positive rates, and scalability are analyzed, with a focus on the advantages and limitations of each approach. Additionally, the paper explores the challenges associated with implementing ML-based IDS, including data quality, adversarial attacks, and computational requirements. Through case studies and real-world examples, this paper highlights how machine learning is revolutionizing intrusion detection and presents insights into future advancements that will further strengthen cybersecurity defenses.