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Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction

A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network's security. Despite this, SDN has a single point of failure that increases the risk of pote...

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Main Authors:	Ahmed, Naveed, Ngadi, Md. Asri, Mohamad Sharif, Johan, Hussain, Saddam, Uddin, Mueen, Rathore, Muhammad Siraj, Iqbal, Jawaid, Abdelhaq, Maha, Alsaqour, Raed, Ullah, Syed Sajid, Fatima Tul Zuhra, Fatima Tul Zuhra
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/104053/1/MdAsriNgadi2022_NetworkThreatDetectionUsingMachine.pdf http://eprints.utm.my/104053/ http://dx.doi.org/10.3390/s22207896
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id	my.utm.104053
record_format	eprints
spelling	my.utm.1040532024-01-14T00:56:21Z http://eprints.utm.my/104053/ Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction Ahmed, Naveed Ngadi, Md. Asri Mohamad Sharif, Johan Hussain, Saddam Uddin, Mueen Rathore, Muhammad Siraj Iqbal, Jawaid Abdelhaq, Maha Alsaqour, Raed Ullah, Syed Sajid Fatima Tul Zuhra, Fatima Tul Zuhra QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network's security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network's integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS. MDPI 2022-10-17 Article PeerReviewed application/pdf en http://eprints.utm.my/104053/1/MdAsriNgadi2022_NetworkThreatDetectionUsingMachine.pdf Ahmed, Naveed and Ngadi, Md. Asri and Mohamad Sharif, Johan and Hussain, Saddam and Uddin, Mueen and Rathore, Muhammad Siraj and Iqbal, Jawaid and Abdelhaq, Maha and Alsaqour, Raed and Ullah, Syed Sajid and Fatima Tul Zuhra, Fatima Tul Zuhra (2022) Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction. Sensors, 22 (20). pp. 1-34. ISSN 1424-8220 http://dx.doi.org/10.3390/s22207896 DOI:10.3390/s22207896
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Ahmed, Naveed Ngadi, Md. Asri Mohamad Sharif, Johan Hussain, Saddam Uddin, Mueen Rathore, Muhammad Siraj Iqbal, Jawaid Abdelhaq, Maha Alsaqour, Raed Ullah, Syed Sajid Fatima Tul Zuhra, Fatima Tul Zuhra Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction
description	A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network's security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network's integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.
format	Article
author	Ahmed, Naveed Ngadi, Md. Asri Mohamad Sharif, Johan Hussain, Saddam Uddin, Mueen Rathore, Muhammad Siraj Iqbal, Jawaid Abdelhaq, Maha Alsaqour, Raed Ullah, Syed Sajid Fatima Tul Zuhra, Fatima Tul Zuhra
author_facet	Ahmed, Naveed Ngadi, Md. Asri Mohamad Sharif, Johan Hussain, Saddam Uddin, Mueen Rathore, Muhammad Siraj Iqbal, Jawaid Abdelhaq, Maha Alsaqour, Raed Ullah, Syed Sajid Fatima Tul Zuhra, Fatima Tul Zuhra
author_sort	Ahmed, Naveed
title	Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction
title_short	Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction
title_full	Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction
title_fullStr	Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction
title_full_unstemmed	Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction
title_sort	network threat detection using machine/deep learning in sdn-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction
publisher	MDPI
publishDate	2022
url	http://eprints.utm.my/104053/1/MdAsriNgadi2022_NetworkThreatDetectionUsingMachine.pdf http://eprints.utm.my/104053/ http://dx.doi.org/10.3390/s22207896
_version_	1789424371370033152
score	13.234276

Network threat detection using machine/deep learning in SDN-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction

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