A DDoS attack detection method based on SVM and K-nearest neighbour in SDN environment
by Zhaohui Ma; Bohong Li
International Journal of Computational Science and Engineering (IJCSE), Vol. 23, No. 3, 2020

Abstract: This paper presents a detection method for DDoS attack in SDN based on K-nearest neighbour (KNN) algorithm and support vector machine (SVM) algorithm. This method makes use of the characteristics of SDN centralised control, collects flow characteristic information efficiently, classifies the flow, screens out the attack flow, and determines whether the system is attacked or not. Experiments show that the resource consumption rate of this model is only 11% when detecting DDOS attack. Meanwhile, the accuracy rate exceeded 99%.

Online publication date: Thu, 26-Nov-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your 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