Enhancing DDoS Attack Detection and Mitigation in SDN Using an Ensemble Online Machine Learning Model-Reference-Cited by-同舟云学术

Enhancing DDoS Attack Detection and Mitigation in SDN Using an Ensemble Online Machine Learning Model

Published:2024 Issue: Volume:12 Page:51630-51649
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Alashhab Abdussalam Ahmed¹^ORCID,Zahid Mohd Soperi²^ORCID,Isyaku Babangida³^ORCID,Elnour Asma Abbas⁴,Nagmeldin Wamda⁵,Abdelmaboud Abdelzahir⁶,Abdullah Talal Ali Ahmed²^ORCID,Maiwada Umar Danjuma²^ORCID

Affiliation:

1. Department of Computer Science, Faculty of Information Technology, Alasmarya Islamic University, Zliten, Libya

2. Department of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

3. Department of Computer Science, Faculty of Computing and Information Technology, Sule Lamido University, Kano, Jigawa, Nigeria

4. Computer Department, Applied College, Girls Section, King Khalid University, Muhayel, Aseer, Saudi Arabia

5. Department of Information Systems, College of Computer Engineering, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

6. Humanities Research Center, Sultan Qaboos University, Seeb, Oman

Funder

Deanship of Scientific Research, King Khalid University, through the Large Research Groups

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10489935.pdf?arnumber=10489935

Reference50 articles.

1. Detection and mitigation of DDoS attacks in SDN: A comprehensive review, research challenges and future directions

2. Experimenting and evaluating the impact of DoS attacks on different SDN controllers

3. New-flow based DDoS attacks in SDN: Taxonomy, rationales, and research challenges

4. An Efficient Scheme to Prevent DDoS Flooding Attacks in Mobile Ad-Hoc Network (MANET)

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