TY  - JOUR
T1  - Machine Learning based IDS for Software Defined Networking
AU - Ghassan Abbas, Oqbah AU - Khorzom, Khaldoun AU - Assora, Mohammed 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 18
SP  - 3354
EP  - 3358
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.3354.3358
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.3354.3358
KW  - SDN
KW  -IDS
KW  -machine learning
KW  -NSL-KDD
KW  -technique
AB  - In the last few years, big companies have been
depending more and more on Software defined
Networking “SDN” to fulfill their needs for
programmable networks. But like other networks, SDN
has some security issues. Many technologies are used to
solve such problems and machine learning is considered
one of the best. Machine learning has demonstrated its
ability to find data patterns when other technologies
failed. This makes it a perfect choice for intrusion
detection system “IDS” in general and anomaly-based
detection in particular. In this research, we propose a new
anomaly-based IDS that benefits from the ability of SDN
to provide statistical features about flows that pass
through the network and passes these features to a voting
system that consists of several machine learning
algorithms. This technique gives the system the ability to
study the user’s behavior and predict any possible
intrusion. The voting system is trained and tested using
NSL-KDD and KDDCup99 datasets and the results shows
increasing in detection accuracy and decreasing in false
positive rate.
ER  -