TY  - EJOU
AU  - Iftikhar, Noveela 
AU  - Rehman, Mujeeb Ur 
AU  - Shah, Mumtaz Ali 
AU  - Alenazi, Mohammed J. F. 
AU  - Ali, Jehad 

TI  - Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 143
IS  - 1
SN  - 1526-1506

AB  - 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.
KW  - Intrusion detection; self-organizing map; Internet of Things; dimensionality reduction

DO  - 10.32604/cmes.2025.062788