Improving DDoS Detection in Software-Defined Networks Through a Hybrid Machine Learning Approach

• Author(s): Francis Onojah ; Prof. Prema Kirubakaran ; Dr. Ridwan Kolapo ; Dr. Temitope Olufunmi Atoyebi ; Dr. R. Renuga Dev
• Paper ID: 1710969
• Page: 1840-1846
• Published Date: 03-10-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 3 September-2025

Abstract

(DDoS) Attacks remain a significant concern for network security, utilizing flood-like traffic at the volume, protocol, and application levels to exploit vulnerabilities in today's infrastructure. To lessen these risks, Software-Defined Networking (SDN) offers programmability and centralized control. However, current machine learning (ML)-based detection techniques have a high false positive rate, are not very flexible against zero-day attacks, and are ineffective when handling high-dimensional flow data. To enhance the detection of DDoS attacks in software-defined networks, this paper proposes a hybrid machine-learning approach. Tapping into SDNs broad view of all network flows, the system studies traffic in real time by merging supervised deep learning- in this case, Long Short-Term Memory- with unsupervised anomaly detection called Isolation Forest. The LSTM sorts incoming packets and learns new normal behavior, while the Isolation Forest flags any stray patterns that don’t fit.

Keywords

DDoS attacks, network security, Long Short-Term Memory (LSTM), CNN

Citations

IRE Journals:
Francis Onojah , Prof. Prema Kirubakaran , Dr. Ridwan Kolapo , Dr. Temitope Olufunmi Atoyebi , Dr. R. Renuga Dev "Improving DDoS Detection in Software-Defined Networks Through a Hybrid Machine Learning Approach" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 1840-1846

IEEE:
Francis Onojah , Prof. Prema Kirubakaran , Dr. Ridwan Kolapo , Dr. Temitope Olufunmi Atoyebi , Dr. R. Renuga Dev "Improving DDoS Detection in Software-Defined Networks Through a Hybrid Machine Learning Approach" Iconic Research And Engineering Journals, 9(3)