Carneiro, VíctorÁlvarez, M. A.Cacheda, Fidel2025-10-022025-10-022025-12V. Carneiro-Diaz, M.A. Álvarez-González, and F. Cacheda-Seijo, "SDN-CF: Traffic classification in SDN ONOS controller using machine learning models", SoftwareX, Vol. 32, Dec. 2025, 102382, https://doi-org.accedys.udc.es/10.1016/j.softx.2025.1023822352-7110https://hdl.handle.net/2183/45865Permanent link to code/repository used for this code version: https://github.com/ElsevierSoftwareX/SOFTX-D-25-00379[Abstract]: SDN-CF (Software-Defined Network - Classification Framework) is a modular Java-based application built on the Northbound API of the ONOS Software-Defined Network (SDN) controller for network traffic analysis using machine learning techniques. While it employs the Random Forest algorithm by default, its open design allows the integration of alternative classifiers. SDN-CF enables the dynamic blocking of unwanted connections and generates an annotated dataset of OpenFlow traffic, supporting reproducible research in anomaly detection. Designed for academic and experimental use in virtualized environments, the tool fosters the evaluation and development of novel detection approaches in SDN contexts.engAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/SDNONOSMachine learningFlow classificationjournal articleopen access10.1016/j.softx.2025.102382