Optimizing network security with SEB-ID: hybrid ensemble learning framework for accurate and scalable anomaly detection

The growing complexity of cyberattacks has made network security a major worry for businesses all around the world. Finding possible network problems and malicious activity requires the application of anomaly detection. These anomalies have been discovered using conventional machine learning (ML) techniques, with models like Random Forest and Convolutional Neural Networks (CNN) showing promise. Yet there are problems with the current approaches, such as poor feature selection, insufficient data pre-processing, and a dearth of efficient ensemble learning strategies. By putting forth a Smart Ensemble-Based Intrusion Detection (SEB-ID) architecture for network anomaly detection, this research aims to overcome these constraints. To increase detection accuracy and robustness, several machine-learning classifiers are combined in the proposed model employing machine-learning classifiers using ensemble-learning approaches, including Decision Tree, Random Forest, ExtraTrees, and XGBoost. The Synthetic Minority Oversampling Technique (SMOTE) is used to balance the dataset, handle missing values, and normalize it as part of a thorough pre-processing phase. To improve model performance, hyperparameter adjustment is proposed via Bayesian optimization. Important performance metrics such as precision, accuracy, F1-score and recall to evaluate SEB-ID model's efficiency, guaranting a strong and dependable detection system.