Assessing the Effectiveness of Machine-Learning Approaches for Detecting Network Attacks: An Empirical Evaluation on NSL-KDD - L. Parthasarathi; Dr. N. Kamalraj (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
Intrusion detection in computer networks is critical for safeguarding sensitive data and maintaining system integrity. This research evaluates the performance of multiple machine-learning models, including Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Multilayer Perceptron (MLP), and XGBoost, for detecting network intrusions using the NSL-KDD dataset. The dataset, comprising 148,517 network traffic instances with 41 features, represents a diverse range of attack types, including DoS, Probe, U2R, and R2L. Preprocessing steps such as normalization, encoding, and feature selection ensure model-ready inputs. Experimental results demonstrate that ensemble and deep-learning-based models outperform traditional classifiers, with XGBoost achieving the highest accuracy, precision, recall, and F1-score. The findings highlight the effectiveness of modern machine-learning approaches in capturing complex attack patterns and improving intrusion-detection performance.