Machine Learning and Deep Learning Techniques for Distributed Denial of Service Anomaly Detection in Software Defined Networks—Current Research Solutions-Reference-Cited by-同舟云学术

Machine Learning and Deep Learning Techniques for Distributed Denial of Service Anomaly Detection in Software Defined Networks—Current Research Solutions

Published:2024 Issue: Volume:12 Page:17982-18011
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Musa Nura Shifa¹^ORCID,Mirza Nada Masood²^ORCID,Rafique Saida Hafsa¹^ORCID,Abdallah Amira Mahamat¹^ORCID,Murugan Thangavel³^ORCID

Affiliation:

1. College of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates

2. College of Engineering, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates

3. Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates

Funder

United Arab Emirates University under Research Start-Up Proposal

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10418146.pdf?arnumber=10418146

Reference52 articles.

1. A DDoS Detection Method Based on Feature Engineering and Machine Learning in Software-Defined Networks

2. Enhancing Network Intrusion Recovery in SDN with machine learning: an innovative approach

3. Mitigation Services on SDN for Distributed Denial of Service and Denial of Service Attacks Using Machine Learning Techniques

4. Detecting flooding DDoS attacks in software defined networks using supervised learning techniques

5. Machine learning algorithms to detect DDoS attacks in SDN

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