A High-Precession Cybersecurity Model with Stacked Ensemble Learning

The successes of Artificial Intelligence (AI) techniques in various fields have significantly increased interest in the use of these technologies in cybersecurity. Although Machine Learning (ML) techniques are effective in detecting malicious activities, certain challenges can negatively impact performance ACC. Developing an effective Intrusion Detection System (IDS) requires the careful selection of appropriate techniques and features. In this study, a Stacking Ensemble Learning (SEL) model is developed to classify network attacks, with Decision Tree (DT), XGBoost, and Multi-Layer Perceptron (MLP) as the base models and Logistic Regression (LR) as the meta-model. Additionally, the K-Fold cross-validation (K-Fold CV) method is employed to improve the generalization capability of the model and prevent overfitting. The SEL model combined with the K-Fold CV method achieved 99.65% ACCon the NSL-KDD dataset and 99.50% ACC on the CICIDS2017 dataset. This demonstrates that combining the SEL model with the K-Fold CV method can achieve high ACCin detecting network attacks in the field of cybersecurity. It also highlights how effective these methods can be in detecting cyber threats.