Systematic Review of Machine Learning-Based DDoS Detection in SDN Networks: A PRISMA Approach

Ananda, Ananda
Suarghana, Yayan

Proceeding Applied Business and Engineering Conference
Vol. 12 (2024): 12th Applied Business and Engineering Conference

This systematic literature review aims to detect the detection of Distributed Denial of Service(DDoS) attacks in Software-Defined Networking (SDN) environments using machine learning techniques. ThePRISMA approach was used to ensure a comprehensive and transparent review process. The underlyingarchitecture of SDN is highly vulnerable to DDoS attacks and thus requires efficient detection mechanisms.This review covers the application of various machine learning algorithms, such as Random Forest, SupportVector Machine (SVM), and Neural Networks, and their effectiveness in identifying anomalous traffic. Datafrom Scopus-indexed journals between 2016 and 2024 is used to provide a comprehensive picture of recentadvances in this field. The research found that machine learning algorithms were able to increase the level ofaccuracy in DDoS detection, but also identified significant challenges such as the limitations of high-qualitydatasets that reflect real network traffic and the need for real-time detection at large network scales. In addition,the computational complexity of deep learning models and resource efficiency in practical applications are alsochallenges that need to be resolved. The results of these observations lead to recommendations for developingmore efficient algorithms, optimizing the use of computing resources, and improving dataset quality to supportmore accurate and faster DDoS detection in SDN environments.