The week's pick
Reseach Article
Machine Learning based Ensemble Technique for DDoS Attack Detection in Software-Defined Networking
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 6 |
| Year of Publication: 2023 |
| Authors: C. Srinivas, P.S. Avadhani, P. Prapoona Roja |
10.5120/ijca2023922712
|
C. Srinivas, P.S. Avadhani, P. Prapoona Roja . Machine Learning based Ensemble Technique for DDoS Attack Detection in Software-Defined Networking. International Journal of Computer Applications. 185, 6 ( May 2023), 22-25. DOI=10.5120/ijca2023922712
Abstract
Next-generation networks can benefit from a more dynamic and successfully controlled network design because of a new network paradigm termed the Software-Defined Network (SDN). Network administrators may simply monitor and manage the entire network using the design of the customizable centralized controller. A number of attack vectors simultaneously target it because of its centralized nature. DDoS attacks are the most efficient type of attack against the SDN. The goal of this work is to classify SDN flow as either normal or assault traffic using ML techniques a public "DDoS attack SDN Dataset" with 23 characteristics in total. The dataset comprises both legitimate and malicious traffic for the TCP, UDP, and ICMP (TCP). The dataset, which includes over 100,000 recordings, offers statistical statistics such byte count, time sec, packet rate, and packet per flow, with the exclusion of characteristics that define source and target devices. In this paper DDoS attack was detected using Various ML Algorithms such as K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms The experimental results demonstrate that an Ensemble Random Forest algorithm was given 99.99% classification accuracy than the other methods
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