Machine Learning in Network Anomaly Detection: A Survey

Anomalies could be the threats to the network that have ever/never happened. To protect networks against malicious access is always challenging even though it has been studied for a long time. Due to the evolution of network in both new technologies and fast growth of connected devices, network atta...

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Autores principales:	Song Wang, Juan Fernando Balarezo, Sithamparanathan Kandeepan, Akram Al-Hourani, Karina Gomez Chavez, Benjamin Rubinstein
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Machine learning anomaly detection network security software defined network Internet of Things cloud network Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/365bbd791aa24ac0a84c447c4d5da395
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id	oai:doaj.org-article:365bbd791aa24ac0a84c447c4d5da395
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spelling	oai:doaj.org-article:365bbd791aa24ac0a84c447c4d5da3952021-11-20T00:01:44ZMachine Learning in Network Anomaly Detection: A Survey2169-353610.1109/ACCESS.2021.3126834https://doaj.org/article/365bbd791aa24ac0a84c447c4d5da3952021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9610045/https://doaj.org/toc/2169-3536Anomalies could be the threats to the network that have ever/never happened. To protect networks against malicious access is always challenging even though it has been studied for a long time. Due to the evolution of network in both new technologies and fast growth of connected devices, network attacks are getting versatile as well. Comparing to the traditional detection approaches, machine learning is a novel and flexible method to detect intrusions in the network, it is applicable to any network structure. In this paper, we introduce the challenges of anomaly detection in the traditional network, as well as in the next generation network, and review the implementation of machine learning in the anomaly detection under different network contexts. The procedure of each machine learning category is explained, as well as the methodologies and advantages are presented. The comparison of using different machine learning models is also summarised.Song WangJuan Fernando BalarezoSithamparanathan KandeepanAkram Al-HouraniKarina Gomez ChavezBenjamin RubinsteinIEEEarticleMachine learninganomaly detectionnetwork securitysoftware defined networkInternet of Thingscloud networkElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152379-152396 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning anomaly detection network security software defined network Internet of Things cloud network Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Machine learning anomaly detection network security software defined network Internet of Things cloud network Electrical engineering. Electronics. Nuclear engineering TK1-9971 Song Wang Juan Fernando Balarezo Sithamparanathan Kandeepan Akram Al-Hourani Karina Gomez Chavez Benjamin Rubinstein Machine Learning in Network Anomaly Detection: A Survey
description	Anomalies could be the threats to the network that have ever/never happened. To protect networks against malicious access is always challenging even though it has been studied for a long time. Due to the evolution of network in both new technologies and fast growth of connected devices, network attacks are getting versatile as well. Comparing to the traditional detection approaches, machine learning is a novel and flexible method to detect intrusions in the network, it is applicable to any network structure. In this paper, we introduce the challenges of anomaly detection in the traditional network, as well as in the next generation network, and review the implementation of machine learning in the anomaly detection under different network contexts. The procedure of each machine learning category is explained, as well as the methodologies and advantages are presented. The comparison of using different machine learning models is also summarised.
format	article
author	Song Wang Juan Fernando Balarezo Sithamparanathan Kandeepan Akram Al-Hourani Karina Gomez Chavez Benjamin Rubinstein
author_facet	Song Wang Juan Fernando Balarezo Sithamparanathan Kandeepan Akram Al-Hourani Karina Gomez Chavez Benjamin Rubinstein
author_sort	Song Wang
title	Machine Learning in Network Anomaly Detection: A Survey
title_short	Machine Learning in Network Anomaly Detection: A Survey
title_full	Machine Learning in Network Anomaly Detection: A Survey
title_fullStr	Machine Learning in Network Anomaly Detection: A Survey
title_full_unstemmed	Machine Learning in Network Anomaly Detection: A Survey
title_sort	machine learning in network anomaly detection: a survey
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/365bbd791aa24ac0a84c447c4d5da395
work_keys_str_mv	AT songwang machinelearninginnetworkanomalydetectionasurvey AT juanfernandobalarezo machinelearninginnetworkanomalydetectionasurvey AT sithamparanathankandeepan machinelearninginnetworkanomalydetectionasurvey AT akramalhourani machinelearninginnetworkanomalydetectionasurvey AT karinagomezchavez machinelearninginnetworkanomalydetectionasurvey AT benjaminrubinstein machinelearninginnetworkanomalydetectionasurvey
_version_	1718419827181223936

Machine Learning in Network Anomaly Detection: A Survey

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