@Article{cmes.2025.062788,
AUTHOR = {Noveela Iftikhar, Mujeeb Ur Rehman, Mumtaz Ali Shah, Mohammed J. F. Alenazi, Jehad Ali},
TITLE = {Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {143},
YEAR = {2025},
NUMBER = {1},
PAGES = {639--671},
URL = {http://www.techscience.com/CMES/v143n1/60475},
ISSN = {1526-1506},
ABSTRACT = {Intrusion attempts against Internet of Things (IoT) devices have significantly increased in the last few years. These devices are now easy targets for hackers because of their built-in security flaws. Combining a Self-Organizing Map (SOM) hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting (XGBoost) for multi-class classification can improve network traffic intrusion detection. The proposed model is evaluated on the NSL-KDD dataset. The hybrid approach outperforms the baseline line models, Multilayer perceptron model, and SOM-KNN (k-nearest neighbors) model in precision, recall, and F1-score, highlighting the proposed approach’s scalability, potential, adaptability, and real-world applicability. Therefore, this paper proposes a highly efficient deployment strategy for resource-constrained network edges. The results reveal that Precision, Recall, and F1-scores rise 10%–30% for the benign, probing, and Denial of Service (DoS) classes. In particular, the DoS, probe, and benign classes improved their F1-scores by 7.91%, 32.62%, and 12.45%, respectively.},
DOI = {10.32604/cmes.2025.062788}
}