Ensemble Approach to Optimize Network Intrusion Detection: A Comparative Study of Machine Learning Classifiers

As networked systems grow quickly, detecting network breaches is becoming more and more important. Network Intrusion Detection Systems (NIDS) are critical tools for the protection of networks from hostile activity, and machine learning based approaches are being increasingly used to improve NIDS effectiveness. This work offers a hybrid ensemble machine learning model that combines three robust classification methods: Random Forest, Gradient Boosting and Support Vector Machines. We test the ensemble model on the KDD Cup 1999 dataset, a standard NIDS benchmark. The model performs better than individual classifiers with an accuracy of 99.14%, precision of 99.05%, recall of 99.14%, and an F1 score of 99.01%. Besides accuracy, precision, recall, and F1 score, the ROC-AUC analysis shows that the ensemble model can discern different types of network attacks. Hyperparameter adjustment and cross validation are investigated in order to optimize model performance better. This work demonstrates the great potential of the ensemble learning approaches to enhance network intrusion detection.