Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model  

作　　者：Noveela Iftikhar Mujeeb Ur Rehman Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali 

机构地区：[1]Knowledge Unit of Systems and Technology,University of Management and Technology,Sialkot,51310,Pakistan [2]Department of Computer Science,University ofWah,Wah Cantt,47010,Pakistan [3]Department of Computer Engineering,College of Computer and Information Sciences(CCIS),King Saud University,Riyadh,11451,Saudi Arabia [4]Department of AI Convergence Network,Ajou University,Suwon,16499,Republic of Korea

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期639-671,共33页工程与科学中的计算机建模(英文)

基　　金：Researcher Supporting Project number(RSPD2025R582),King Saud University,Riyadh,Saudi Arabia.

摘　　要：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.

关 键 词：Intrusion detection self-organizing map Internet of Things dimensionality reduction 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]