The research spotlight continues to shine on AiNetSeC Lab with another significant publication, this time accepted at the ICIT 2025 conference. Our paper, A Survey of Supervised, Unsupervised, and Ensemble Learning Approaches for DDoS Detection in SDN, addresses the critical need for robust security in software-defined networks (SDN).
The study provides a thorough survey of intelligent machine learning models for detecting Distributed Denial-of-Service (DDoS) attacks in modern network infrastructures.
Why this matters:
With the growing dependency on SDN for flexible and programmable networks, DDoS attacks pose a significant threat. This research offers a comparative lens on current solutions and identifies the most promising techniques for future use.
Acknowledgment:
We’re grateful for the input from all AiNetSeC members and external collaborators. This work was independently conducted with no funding received.
Stay tuned for access on IEEE Xplore, Scopus, and Google Scholar.