Machine learning in OpenFlow network : comparative analysis of DDoS detection techniques
Source
The International Arab Journal of Information Technology
Issue
Vol. 18, Issue 2 (31 Mar. 2021), pp.221-226, 6 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2021-03-31
Country of Publication
Jordan
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Abstract EN
Software Defined Network (SDN) allows the separation of a control layer and data forwarding at two different layers.
However, centralized control systems in SDN is vulnerable to attacks namely Distributed Denial of Service (DDoS).
Therefore, it is necessary for developing a solution based on reactive applications that can identify, detect, as well as mitigate the attacks comprehensively.
In this paper, an application has been built based on machine learning methods including, Support Vector Machine (SVM) using Linear and Radial Basis Function kernel, K-Nearest Neighbor (KNN), Decision Tree (DTC), Random Forest (RFC), Multi-Layer Perceptron (MLP), and Gaussian Naïve Bayes (GNB).
The paper also proposed a new scheme of DDOS dataset in SDN by gathering considerably static data form using the port statistic.
SVM became the most efficient method for identifying DDoS attack successfully proved by the accuracy, precision, and recall approximately 100 % which could be considered as the primary algorithm for detecting DDoS.
In term of the promptness, KNN had the slowest rate for the whole process, while the fastest was depicted by GNB.
American Psychological Association (APA)
Singh, Arun Kumar. 2021. Machine learning in OpenFlow network : comparative analysis of DDoS detection techniques. The International Arab Journal of Information Technology،Vol. 18, no. 2, pp.221-226.
https://search.emarefa.net/detail/BIM-1430920
Modern Language Association (MLA)
Singh, Arun Kumar. Machine learning in OpenFlow network : comparative analysis of DDoS detection techniques. The International Arab Journal of Information Technology Vol. 18, no. 2 (Mar. 2021), pp.221-226.
https://search.emarefa.net/detail/BIM-1430920
American Medical Association (AMA)
Singh, Arun Kumar. Machine learning in OpenFlow network : comparative analysis of DDoS detection techniques. The International Arab Journal of Information Technology. 2021. Vol. 18, no. 2, pp.221-226.
https://search.emarefa.net/detail/BIM-1430920
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 225-226
Record ID
BIM-1430920