Implementing an Optimized Voting Ensemble Model: A Step Towards Advanced Network Intrusion Detection Solutions

A significant problem for modern communication systems is accurate and real-time intrusion detection, which is especially important given the rapid expansion of network infrastructures and the rising sophistication of cyberattacks. A network-based intrusion detection system that can adapt to changing and unbalanced traffic conditions is the focus of this research. In order to improve the performance of intrusion detection, a voting-based ensemble machine learning, the VEML framework that uses the ensemble machine learning mechanism and Brown Bear Optimization (BBO), is used for optimization. The suggested VEML uses AdaBoost and XGBoost. and Incremental Support Vector Machine (ISVM) in an ensemble way for classification. After implementing a uniform preprocessing pipeline that includes normalizing the Z-score and Min-Max, we use SMOTE to mitigate class imbalance. In order to decrease dimensionality and increase computing efficiency, feature selection is carried out using an ensemble-based approach that combines DT, RF, and LR.For hyperparameter tweaking that is classifier-specific, BBO is used to provide steady convergence and better generalization.Four benchmark datasets CICIDS2017, CSE-CIC-IDS2018, NSL-KDD, and UNSW-NB15 are used to test the framework. With an F1-score of 99.04% and an accuracy of 99.6% on CIC-IDS2017, the suggested model also manages a precision of 99.0% and a recall of 99.01%.The outcomes prove that the VEMLBBO architecture is an effective, efficient, and practical way to identify intrusions in current networks.