Enhancing Network Security with Tree-Based Machine Learning: A Study on Ensemble Intrusion Detection System Models

In the era of rapid technological advancement, cybersecurity has become a paramount concern, with the ever-growing threat of unauthorized access, data breaches, and malicious attacks on networks. In this paper, we proposed an advanced intrusion detection system (IDS) leveraging various machine learning ensemble learning techniques. Intrusion detection systems are critical for cybersecurity, identifying and mitigating unauthorized access and attacks on networks. By utilizing models such as Decision Tree, Random Forest, Gradient Boosting, and XGBoost, we aimed to enhance the accuracy and efficiency of detecting network intrusions.