Ensemble learning has been shown to be an effective approach for improving the accuracy of intrusion detection systems (IDSs). In this paper, we propose two ensemble learning models for IDS, based on voting and stacking. We evaluate our models on the NSL-KDD dataset, and show that they can achieve significant improvements in accuracy over single classifiers. Single classifiers have several limitations that can lead to poor performance when classifying normal and abnormal network traffic. For example, they may be sensitive to noise or outliers, or they may not be able to generalize well to new data. Ensemble learning can address these limitations by combining the predictions of multiple classifiers. This can help to reduce the variance of the predictions, and improve the overall accuracy of the system. In our experiments, we show that our ensemble learning models can achieve an accuracy of up to 99% on the NSL-KDD dataset. This is a significant improvement over single classifiers, which typically achieve accuracies of around 90%. We also show that our models can achieve a false alarm rate (FAR) of less than 1%. This means that they are very good at detecting intrusions, while also minimizing the number of false alarms. Our results suggest that ensemble learning is a promising approach for improving the accuracy of IDSs. We believe that our models can be used to improve the security of computer networks, and we plan to continue our research in this area.

ensemble learning, voting, stacking, particle swarm optimization, intrusion detection system, network security, machine learning