A Comprehensive approach to intelligent intrusion Detection in cyber security using machine learning

Abstract

Security Operations Centers (SOCs) play a critical role in defending organizations against evolving cyber threats, yet traditional SOC environments struggle with high alert volumes, excessive false positives, and fragmented data sources, which delay timely threat response. This study proposes a User-Centric Machine Learning Framework designed to enhance cyber defense by integrating advanced machine learning techniques with humancentered design principles. The proposed framework collects and preprocesses multi-source security data, including network traffic, endpoint logs, and threat intelligence feeds, to enable efficient and unified analysis. Machine learning algorithms such as decision trees, random forests, and anomaly detection models are employed to classify threats and identify suspicious behavior patterns. Natural Language Processing (NLP) techniques are applied to analyze unstructured security reports and phishing content, extracting actionable threat intelligence. Interactive visualization dashboards reduce analyst workload by presenting prioritized alerts and contextual threat information, while continuous feedback from analysts improves model accuracy through adaptive learning and retraining mechanisms. Predictive analytics and automated response systems further support proactive threat mitigation and rapid incident handling. Explainable AI and role-based access control are incorporated to ensure transparency, security, and trust in automated decisions. Overall, the proposed framework improves SOC efficiency, reduces response time, minimizes false positives, and strengthens organizational cybersecurity resilience.