Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This literature survey explores the landscape of Distributed Denial of Service (DDoS) detection and mitigation techniques tailored for Software-Defined Networks (SDNs). In the face of escalating DDoS threats, particularly within the dynamic environment of SDNs, the necessity for effective detection and mitigation strategies is paramount. Through a comprehensive examination of existing research, this paper elucidates the diverse array of methodologies employed for detecting and mitigating DDoS attacks in SDNs, spanning from traditional to emerging approaches. By analysing the strengths, limitations, and comparative efficacy of these techniques, this survey aims to provide insights into the evolving strategies for fortifying SDNs against DDoS assaults. Additionally, it identifies existing gaps and potential avenues for future research, with the goal of fostering advancements in DDoS defence mechanisms within SDN infrastructures.
Keywords:
DDoS, Deep Learning, Machine Learning, Software Defined Networks.
Cite Article:
"Enhancing DDoS Detection and Mitigation in Software-Defined Networks: A Literature Survey", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.258 - 262, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405038.pdf
Downloads:
000205099
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator