Optimized Random Forest Framework for Cybersecurity Anomaly Detection

The resreach of cybersecurity anomaly detection has gained significant research importance because sensitive systems and networks face growing complex and frequent cyber threats. Traditional signature-based intrusion detection systems struggle to detect zero-day attacks because they create significant false positives in their analysis. A new cybersecurity framework employs Random Forest for robust anomaly detection through this research. The detection system performs extensive preprocessing through statistical methods as well as Synthetic Minority Oversampling Technique (SMOTE) to deal with unbalanced data classes. Using GridSearchCV operators enables users to achieve maximum model performance. The system demonstrates detection accuracy above 93% which was validated through precision, recall, F1-score and ROC-AUC metrics. The detection of important features through analysis reveals major attributes which support anomaly detection processes while improving both interpretation abilities and decision-making systems. The analysis between Random Forest and Logistic Regression and Support Vector Machines and K-Nearest Neighbors and Neural Networks demonstrates Random Forest delivers superior accuracy along with robust performance. Real-time detection capabilities and explainable AI methods now have groundwork from this study which demonstrates Random Forest's crucial role in cyber security protection.