Artificial Intelligence and Communication Technologies
Comparative Analysis of DDoS Attacks Detection Systems in Software defined Networks
Authors: Anuja Sharma and Parul Saxena
Publishing Date: 09-09-2022
ISBN: 978-81-955020-5-9
Abstract
The software-Defined Network (SDN) is the pre-eminent network framework in recent decades as it ensures more authority over the recent network architecture. The Controller, which is characterized as the system software of the SDN is liable for running different organization applications and conserving a few organization administrations and functionalities. In spite of all its potential, the establishment of numerous constructive organizations of SDN creates numerous security dangers and possible targets. The Distributed Denial of Services (DDoS) is one of the major security threats that deteriorate the performance of the SDN organization. More researchers are concentrated to restrain the DDoS attack as the control layer in the SDN is the most exposed to DDoS attacks. These days, in the field of SDN, different AI (ML) procedures are being conveyed to recognize DDoS attack. Hence in this paper, 15 papers related to DDoS attack detection are analyzed. The evaluation of the research is implemented with respect to the various factors such as performance metrics, achievement of the existing methods, classifier or the methods utilized and so on. Finally, this report elucidates the future direction of the research.
Keywords
SDN, Security, DDoS attacks, Machine learning techniques, SVM
Cite as
Anuja Sharma and Parul Saxena, "Comparative Analysis of DDoS Attacks Detection Systems in Software defined Networks", In: Saroj Hiranwal and Garima Mathur (eds), Artificial Intelligence and Communication Technologies, SCRS, India, 2022, pp. 283-296. https://doi.org/10.52458/978-81-955020-5-9-29