Machine Learning Models for DDoS Attack Detection: A Systematic Literature Review

Chinyere Chioma Isiekwene (Department of Computer Sciences, Faculty of Science, University of Lagos, Nigeria)
Nureni Ayofe Azeez (Department of Computer Sciences, Faculty of Science, University of Lagos, Nigeria)
Solomon A. Akinboro (Department of Computer Sciences, Faculty of Science, University of Lagos, Nigeria)
Oladipupo Sennaike (Department of Computer Sciences, Faculty of Science, University of Lagos, Nigeria)

Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Vol. 3 No. 2 (2026): (In Progress)

The study aims to present a detailed analysis of different machine learning models used in the detection of distributed denial of service (DDoS) attacks. The report adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) style to determine the research domain, established a search list, and analyzed all the selected articles from scientific databases such as IEEE, Springer, Elsevier, MDPI, SSRN-JETIR, Wiley online-library, and Google Scholar to meet eligibility criteria. A total of 6560 articles were retrieved, and 75 were deemed eligible for study. The review identified seven subject categories in the literature review, and the results show that 48% of the reviewed papers were from Elsevier (Science Direct), IEEE covered 20%, Springer covered 16%, while MDPI count was 10.67%. 2023 had the highest number of paper sources, followed closely by 2022, then 2024. The study reveals the milestone achieved in the use of machine learning models in detecting distributed denial of service attacks alongside the existing gap in the application of these models.