Cover Image

Machine learning-based anomaly detection in NFV: a comprehensive survey

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT...

Full description

Saved in:
Bibliographic Details
Main Authors: Sehar Zehra, Ummay Faseeha, Hassan Jamil Syed, Fahad Samad, Ashraf Osman Ibrahim Elsayed, Anas W. Abulfaraj, Wamda Nagmeldin
Format: Article
Language:English
English
Published: MDPI 2023
Subjects:
Q300-390 Cybernetics
TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Online Access:https://eprints.ums.edu.my/id/eprint/38205/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/38205/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/38205/
https://www.mdpi.com/1424-8220/23/11/5340#
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ums.eprints.38205
record_format eprints
spelling my.ums.eprints.382052024-02-09T03:13:53Z https://eprints.ums.edu.my/id/eprint/38205/ Machine learning-based anomaly detection in NFV: a comprehensive survey Sehar Zehra Ummay Faseeha Hassan Jamil Syed Fahad Samad Ashraf Osman Ibrahim Elsayed Anas W. Abulfaraj Wamda Nagmeldin Q300-390 Cybernetics TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learningbased algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems. MDPI 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/38205/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/38205/2/FULL%20TEXT.pdf Sehar Zehra and Ummay Faseeha and Hassan Jamil Syed and Fahad Samad and Ashraf Osman Ibrahim Elsayed and Anas W. Abulfaraj and Wamda Nagmeldin (2023) Machine learning-based anomaly detection in NFV: a comprehensive survey. Sensors, 23. pp. 1-26. ISSN 1996-2022 https://www.mdpi.com/1424-8220/23/11/5340#
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic Q300-390 Cybernetics
TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle Q300-390 Cybernetics
TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Sehar Zehra
Ummay Faseeha
Hassan Jamil Syed
Fahad Samad
Ashraf Osman Ibrahim Elsayed
Anas W. Abulfaraj
Wamda Nagmeldin
Machine learning-based anomaly detection in NFV: a comprehensive survey
description Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learningbased algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.
format Article
author Sehar Zehra
Ummay Faseeha
Hassan Jamil Syed
Fahad Samad
Ashraf Osman Ibrahim Elsayed
Anas W. Abulfaraj
Wamda Nagmeldin
author_facet Sehar Zehra
Ummay Faseeha
Hassan Jamil Syed
Fahad Samad
Ashraf Osman Ibrahim Elsayed
Anas W. Abulfaraj
Wamda Nagmeldin
author_sort Sehar Zehra
title Machine learning-based anomaly detection in NFV: a comprehensive survey
title_short Machine learning-based anomaly detection in NFV: a comprehensive survey
title_full Machine learning-based anomaly detection in NFV: a comprehensive survey
title_fullStr Machine learning-based anomaly detection in NFV: a comprehensive survey
title_full_unstemmed Machine learning-based anomaly detection in NFV: a comprehensive survey
title_sort machine learning-based anomaly detection in nfv: a comprehensive survey
publisher MDPI
publishDate 2023
url https://eprints.ums.edu.my/id/eprint/38205/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/38205/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/38205/
https://www.mdpi.com/1424-8220/23/11/5340#
_version_ 1792152910564425728
score 13.211869

Similar Items