@Article{cmes.2022.020724,
AUTHOR = {Gulshan Kumar, Hamed Alqahtani},
TITLE = {Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {1},
PAGES = {89--119},
URL = {http://www.techscience.com/CMES/v134n1/49430},
ISSN = {1526-1506},
ABSTRACT = {Software-Defined Networking (SDN) enables flexibility in developing security tools that can effectively and
efficiently analyze and detect malicious network traffic for detecting intrusions. Recently Machine Learning (ML)
techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems
(IDSs) considering logically centralized control and global view of the network provided by SDN. Many IDSs have
developed using advances in machine learning and deep learning. This study presents a comprehensive review of
recent work of ML-based IDS in context to SDN. It presents a comprehensive study of the existing review papers in
the field. It is followed by introducing intrusion detection, ML techniques and their types. Specifically, we present
a systematic study of recent works, discuss ongoing research challenges for effective implementation of ML-based
intrusion detection in SDN, and promising future works in this field.},
DOI = {10.32604/cmes.2022.020724}
}