Kanwal, Khadija and Mujahid, Muhammad and Martínez Espinosa, Julio César and Uc Ríos, Carlos Eduardo and Samee, Nagwan Abdel and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, ulio.martinez@unini.edu.mx, carlos.uc@unini.edu.mx, UNSPECIFIED, UNSPECIFIED (2026) LwHM: lightweight hybrid classifier for SDN-attack detection using recursive feature elimination. Scientific Reports. ISSN 2045-2322
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Abstract
Internet connectivity has significantly enhanced the efficiency of daily operations, information retrieval, and global communication. However, this heightened reliance on technology has also exposed us to cybersecurity threats that are often beyond our control. Consequently, securing the data, privacy, and critical systems demands essential cybersecurity measures. This study focuses on the role of artificial intelligence in strengthening security systems to thwart network breaches. The study proposes a comprehensive three-part approach for software-defined networking (SDN) security. The first is that the concentration is on assuring data integrity and reliability for an SDN intrusion dataset. This involves critical steps such as data cleaning, preprocessing, and normalization. In the second step, six popular feature selection strategies are applied, which encompass recursive feature elimination (RFE), polynomial features, artificial neural networks, SelectKBest, least absolute shrinkage and selection operator (LASSO), and correlation-based features. These techniques help identify and incorporate significant and relevant features, thereby improving the overall model performance. The third part involves the creation of a lightweight hybrid model (LwHM) that leverages the strengths of k-nearest neighbors and decision tree models, utilizing a voting classifier. The LwHM surpasses the performance of the InSDN dataset, achieved an impressive accuracy score of 99.93% with RFE features, and enhance the SDN security efficiently.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Network intrusion detection Network security Software defined networks Hybrid model Machine learning Feature selection |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Articles and books Fundación Universitaria Internacional de Colombia > Research > Articles and books Ibero-american International University > Research > Articles and books Ibero-american International University > Research > Articles and Books Universidad Internacional do Cuanza > Research > Articles and books University of La Romana > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 23 Jul 2026 06:32 |
| Last Modified: | 23 Jul 2026 06:32 |
| URI: | https://repositorio.funiber.org/id/eprint/28948 |
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