Comprehensive Approach to Intelligent Intrusion Detection in Cyber Security Using Machine Learning

Abstract

Cybersecurity attacks are growing at an unprecedented rate, making conventional Security Operations Centers (SOCs) increasingly inefficient due to overwhelming alert volumes, elevated false-positive rates, and isolated security data sources. This paper introduces a user-focused machine learning framework for intelligent intrusion detection in cybersecurity systems. The proposed approach combines multiple sources of security information, including network traffic data, endpoint activity logs, and threat intelligence feeds, to enable comprehensive threat assessment. Machine learning techniques such as Decision Trees, Random Forests, and anomaly detection algorithms are utilized to detect malicious activities and classify potential threats. Natural Language Processing (NLP) methods are applied to extract meaningful information from unstructured sources, including phishing emails, threat reports, and security documents. The framework also incorporates interactive visualization dashboards that support analysts in alert prioritization and enhance decision-making capabilities. Continuous feedback and adaptive learning mechanisms facilitate model refinement and retraining, leading to improved detection performance over time. Furthermore, explainable artificial intelligence and role-based access control mechanisms are integrated to promote transparency, accountability, and trust in automated security decisions. The proposed framework effectively decreases false-positive alerts, accelerates threat response, and enhances the overall resilience of cybersecurity infrastructures.