A Unified Machine Learning Approach for Real-Time Intrusion, Malware, and URL Detection Using Random Forest and Advanced Algorthims

Abstract

The research develops a single cybersecurity framework that executes real-time attack detection on intrusions and malware as well as dangerous URLs through machine learning methods. A combination of ensemble learning approaches primarily includes Random Forest together with Gradient Boosting and Naive Bayes and Support Vector Machines (SVM) exists to provide full-scale threat detection capabilities. The system employs Principal Component Analysis (PCA) together with other dimensionality reduction methods for better processing performance. The framework achieves 99% accuracy together with 94% precision, 91% recall and a 95% F1-score during evaluation. The proposed approach provides instant classification and implements automated response functions which differ from delayed reactive systems that need manual human involvement. The framework demonstrates future readiness through its design that supports growth and it will enhance its capabilities by integrating deep learning models together with adaptive learning systems during its development phase. This integrated method unites numerous safety detection systems into one unified solution leading to improved cybersecurity functions beside decreased tool dependence for security operations.