An Essay on Detailed Performance Assessment Ensemble-Based Predictive Modelling in Network Intrusion Detection Systems

Network Intrusion Detection Systems (NIDS) play a critical role in safeguarding computer networks against malicious activities and cyber threats. To improve the accuracy and robustness of NIDS, this research explores the effectiveness of ensemble-based predictive modelling. Ensembles combine multiple individual machine learning models to make collective predictions, potentially outperforming single models. The study involves designing and implementing various ensemble techniques, such as Random Forest, Gradient Boosting, and Stacking, and comparing their performance against individual models using a well-curated NIDS dataset. The research findings demonstrate that ensembles show promising results in enhancing NIDS accuracy, especially in detecting various types of network intrusions. The analysis sheds light on the strengths and limitations of ensemble approaches and provides valuable insights for deploying machine learning ensembles in real-world NIDS scenarios to bolster network security and protect against emerging cyber threats.