Intelligent DDoS Attack Detection in Software-Defined Networks Using Explainable Machine Learning
Javaid Ahmad Malik, Naila Samar Naz, Muhammad Saleem, Muhammad Adnan Khan · 2026
Read Intelligent DDoS Attack Detection in Software-Defined Networks Using Explainable Machine Learning by Javaid Ahmad Malik, Naila Samar Naz, Muhammad Saleem, Muhammad Adnan Khan (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13568309.txt.
Description
Javaid Ahmad Malik and co-authors built a detection system for denial-of-service attacks on software-defined networks. In such networks one central controller directs traffic, which makes management flexible but also creates an inviting target. Attackers can flood the network with junk traffic until real services fail. Older rule-based detectors miss new attack patterns and raise many false alarms, while standard learning models cannot explain their decisions.
The team trained several models on more than one hundred thousand network flow records and used explanation tools to show which traffic features drove each verdict. Gradient Boosting performed best, reaching about 99.9 percent test accuracy. The explanations give analysts visible evidence for each alert instead of a bare score.
Catalog record
| CREATOR | Javaid Ahmad Malik, Naila Samar Naz, Muhammad Saleem, Muhammad Adnan Khan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |