Machine learning based low-rate DDoS attack detection for SDN enabled IoT networks Online publication date: Mon, 21-Sep-2020
by Haosu Cheng; Jianwei Liu; Tongge Xu; Bohan Ren; Jian Mao; Wei Zhang
International Journal of Sensor Networks (IJSNET), Vol. 34, No. 1, 2020
Abstract: The software-defined network (SDN) enabled internet of things (IoT) architecture is deployed in many industrial systems. The ability of SDN to intelligently route traffic and use underutilised network resources, enables IoT networks to cope with data onslaught smoothly. SDN also eliminates bottlenecks and helps to process IoT data efficiently without placing a larger strain on the network. The SDN-based IoT network is vulnerable to DDoS attack in a sophisticated usage environment. The SDN-based IoT network behaviours are different from traditional networks, which makes the detection of low-traffic DDoS attacks more difficult. In this paper, we propose a learning-based detection approach that deploys learning algorithms and utilizes stateful and stateless features from Openflow packages to identify attack traffics in SDN control and data planes. Our prototype approach and experiment results show that our system identified the low-rate DDoS attack traffic accurately with relatively low system performance overheads.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook