Recursive Feature Selection and Intrusion Classification in NSL-KDD Dataset Using Multiple Machine Learning Methods

IDS are critical components of modern computer networks, designed to detect and alert administrators of malicious activity. In order to detect network irregularities and keep data secure, it is critical to build an effective IDS that prevents unauthorized access to network resources. In this study, several machine learning classifiers were used to detect attacks in the NSL-KDD dataset. These classifiers included SVM, Naive Bayes, Random Forest, Decision Tree, and XGBoost. We have chosen 13 feature subsets using the recursive feature selection technique from the NSL-KDD dataset and used them to assess the model’s performance. Because the dimension of the data influences how well this IDS performs, the data was pre-processed, and superfluous attributes were deleted. The experimental results demonstrate that for all attack classes utilizing distinctive feature subsets, the accuracy of Decision Tree (DT), Nave Bayes (NB), Random Forest (RF), Linear Regression, XGBoost, AdaBoost, and Support Vector Machine (SVM) was over 95%. Overall, the performance of XGBoost in conjunction with recursive feature selection was the best.