A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks

The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hela Mliki [verfasserIn] Abir Kaceam [verfasserIn] Lamia Chaari [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	internet of things (iot) wireless sensor network (wsn) machine learning (ml) intrusion detection (id)

Übergeordnetes Werk:	In: EAI Endorsed Transactions on Security and Safety - European Alliance for Innovation (EAI), 2016, 8(2021), 29
Übergeordnetes Werk:	volume:8 ; year:2021 ; number:29

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.4108/eai.6-10-2021.171246

Katalog-ID:	DOAJ061266019

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ061266019
003	DE-627
005	20230503070731.0
007	cr uuu---uuuuu
008	230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.4108/eai.6-10-2021.171246 \|2 doi
035			\|a (DE-627)DOAJ061266019
035			\|a (DE-599)DOAJ0e738de4a5c64bd08fe69765dcbcfd79
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Hela Mliki \|e verfasserin \|4 aut
245	1	2	\|a A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions.
650		4	\|a internet of things (iot)
650		4	\|a wireless sensor network (wsn)
650		4	\|a machine learning (ml)
650		4	\|a intrusion detection (id)
653		0	\|a Technology
653		0	\|a T
700	0		\|a Abir Kaceam \|e verfasserin \|4 aut
700	0		\|a Lamia Chaari \|e verfasserin \|4 aut
773	0	8	\|i In \|t EAI Endorsed Transactions on Security and Safety \|d European Alliance for Innovation (EAI), 2016 \|g 8(2021), 29 \|w (DE-627)1685371272 \|x 20329393 \|7 nnns
773	1	8	\|g volume:8 \|g year:2021 \|g number:29
856	4	0	\|u https://doi.org/10.4108/eai.6-10-2021.171246 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/0e738de4a5c64bd08fe69765dcbcfd79 \|z kostenfrei
856	4	0	\|u https://eudl.eu/pdf/10.4108/eai.6-10-2021.171246 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2032-9393 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 8 \|j 2021 \|e 29

Indexfelder

author_variant	h m hm a k ak l c lc
matchkey_str	article:20329393:2021----::cmrhnieuvynnrsodtcinaemciee
hierarchy_sort_str	2021
publishDate	2021
allfields	10.4108/eai.6-10-2021.171246 doi (DE-627)DOAJ061266019 (DE-599)DOAJ0e738de4a5c64bd08fe69765dcbcfd79 DE-627 ger DE-627 rakwb eng Hela Mliki verfasserin aut A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions. internet of things (iot) wireless sensor network (wsn) machine learning (ml) intrusion detection (id) Technology T Abir Kaceam verfasserin aut Lamia Chaari verfasserin aut In EAI Endorsed Transactions on Security and Safety European Alliance for Innovation (EAI), 2016 8(2021), 29 (DE-627)1685371272 20329393 nnns volume:8 year:2021 number:29 https://doi.org/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/article/0e738de4a5c64bd08fe69765dcbcfd79 kostenfrei https://eudl.eu/pdf/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/toc/2032-9393 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 29
spelling	10.4108/eai.6-10-2021.171246 doi (DE-627)DOAJ061266019 (DE-599)DOAJ0e738de4a5c64bd08fe69765dcbcfd79 DE-627 ger DE-627 rakwb eng Hela Mliki verfasserin aut A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions. internet of things (iot) wireless sensor network (wsn) machine learning (ml) intrusion detection (id) Technology T Abir Kaceam verfasserin aut Lamia Chaari verfasserin aut In EAI Endorsed Transactions on Security and Safety European Alliance for Innovation (EAI), 2016 8(2021), 29 (DE-627)1685371272 20329393 nnns volume:8 year:2021 number:29 https://doi.org/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/article/0e738de4a5c64bd08fe69765dcbcfd79 kostenfrei https://eudl.eu/pdf/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/toc/2032-9393 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 29
allfields_unstemmed	10.4108/eai.6-10-2021.171246 doi (DE-627)DOAJ061266019 (DE-599)DOAJ0e738de4a5c64bd08fe69765dcbcfd79 DE-627 ger DE-627 rakwb eng Hela Mliki verfasserin aut A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions. internet of things (iot) wireless sensor network (wsn) machine learning (ml) intrusion detection (id) Technology T Abir Kaceam verfasserin aut Lamia Chaari verfasserin aut In EAI Endorsed Transactions on Security and Safety European Alliance for Innovation (EAI), 2016 8(2021), 29 (DE-627)1685371272 20329393 nnns volume:8 year:2021 number:29 https://doi.org/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/article/0e738de4a5c64bd08fe69765dcbcfd79 kostenfrei https://eudl.eu/pdf/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/toc/2032-9393 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 29
allfieldsGer	10.4108/eai.6-10-2021.171246 doi (DE-627)DOAJ061266019 (DE-599)DOAJ0e738de4a5c64bd08fe69765dcbcfd79 DE-627 ger DE-627 rakwb eng Hela Mliki verfasserin aut A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions. internet of things (iot) wireless sensor network (wsn) machine learning (ml) intrusion detection (id) Technology T Abir Kaceam verfasserin aut Lamia Chaari verfasserin aut In EAI Endorsed Transactions on Security and Safety European Alliance for Innovation (EAI), 2016 8(2021), 29 (DE-627)1685371272 20329393 nnns volume:8 year:2021 number:29 https://doi.org/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/article/0e738de4a5c64bd08fe69765dcbcfd79 kostenfrei https://eudl.eu/pdf/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/toc/2032-9393 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 29
allfieldsSound	10.4108/eai.6-10-2021.171246 doi (DE-627)DOAJ061266019 (DE-599)DOAJ0e738de4a5c64bd08fe69765dcbcfd79 DE-627 ger DE-627 rakwb eng Hela Mliki verfasserin aut A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions. internet of things (iot) wireless sensor network (wsn) machine learning (ml) intrusion detection (id) Technology T Abir Kaceam verfasserin aut Lamia Chaari verfasserin aut In EAI Endorsed Transactions on Security and Safety European Alliance for Innovation (EAI), 2016 8(2021), 29 (DE-627)1685371272 20329393 nnns volume:8 year:2021 number:29 https://doi.org/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/article/0e738de4a5c64bd08fe69765dcbcfd79 kostenfrei https://eudl.eu/pdf/10.4108/eai.6-10-2021.171246 kostenfrei https://doaj.org/toc/2032-9393 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 29
language	English
source	In EAI Endorsed Transactions on Security and Safety 8(2021), 29 volume:8 year:2021 number:29
sourceStr	In EAI Endorsed Transactions on Security and Safety 8(2021), 29 volume:8 year:2021 number:29
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	internet of things (iot) wireless sensor network (wsn) machine learning (ml) intrusion detection (id) Technology T
isfreeaccess_bool	true
container_title	EAI Endorsed Transactions on Security and Safety
authorswithroles_txt_mv	Hela Mliki @@aut@@ Abir Kaceam @@aut@@ Lamia Chaari @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	1685371272
id	DOAJ061266019
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ061266019</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503070731.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4108/eai.6-10-2021.171246</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ061266019</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ0e738de4a5c64bd08fe69765dcbcfd79</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hela Mliki</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">internet of things (iot)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">wireless sensor network (wsn)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning (ml)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">intrusion detection (id)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Abir Kaceam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lamia Chaari</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">EAI Endorsed Transactions on Security and Safety</subfield><subfield code="d">European Alliance for Innovation (EAI), 2016</subfield><subfield code="g">8(2021), 29</subfield><subfield code="w">(DE-627)1685371272</subfield><subfield code="x">20329393</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:29</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.4108/eai.6-10-2021.171246</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/0e738de4a5c64bd08fe69765dcbcfd79</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://eudl.eu/pdf/10.4108/eai.6-10-2021.171246</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2032-9393</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2021</subfield><subfield code="e">29</subfield></datafield></record></collection>
author	Hela Mliki
spellingShingle	Hela Mliki misc internet of things (iot) misc wireless sensor network (wsn) misc machine learning (ml) misc intrusion detection (id) misc Technology misc T A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks
authorStr	Hela Mliki
ppnlink_with_tag_str_mv	@@773@@(DE-627)1685371272
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	20329393
topic_title	A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks internet of things (iot) wireless sensor network (wsn) machine learning (ml) intrusion detection (id)
topic	misc internet of things (iot) misc wireless sensor network (wsn) misc machine learning (ml) misc intrusion detection (id) misc Technology misc T
topic_unstemmed	misc internet of things (iot) misc wireless sensor network (wsn) misc machine learning (ml) misc intrusion detection (id) misc Technology misc T
topic_browse	misc internet of things (iot) misc wireless sensor network (wsn) misc machine learning (ml) misc intrusion detection (id) misc Technology misc T
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	EAI Endorsed Transactions on Security and Safety
hierarchy_parent_id	1685371272
hierarchy_top_title	EAI Endorsed Transactions on Security and Safety
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)1685371272
title	A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks
ctrlnum	(DE-627)DOAJ061266019 (DE-599)DOAJ0e738de4a5c64bd08fe69765dcbcfd79
title_full	A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks
author_sort	Hela Mliki
journal	EAI Endorsed Transactions on Security and Safety
journalStr	EAI Endorsed Transactions on Security and Safety
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
author_browse	Hela Mliki Abir Kaceam Lamia Chaari
container_volume	8
format_se	Elektronische Aufsätze
author-letter	Hela Mliki
doi_str_mv	10.4108/eai.6-10-2021.171246
author2-role	verfasserin
title_sort	comprehensive survey on intrusion detection based machine learning for iot networks
title_auth	A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks
abstract	The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions.
abstractGer	The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions.
abstract_unstemmed	The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	29
title_short	A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks
url	https://doi.org/10.4108/eai.6-10-2021.171246 https://doaj.org/article/0e738de4a5c64bd08fe69765dcbcfd79 https://eudl.eu/pdf/10.4108/eai.6-10-2021.171246 https://doaj.org/toc/2032-9393
remote_bool	true
author2	Abir Kaceam Lamia Chaari
author2Str	Abir Kaceam Lamia Chaari
ppnlink	1685371272
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.4108/eai.6-10-2021.171246
up_date	2024-07-03T19:45:39.863Z
_version_	1803588405695610880
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ061266019</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503070731.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4108/eai.6-10-2021.171246</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ061266019</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ0e738de4a5c64bd08fe69765dcbcfd79</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hela Mliki</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols.The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able toenhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitivedata and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of researchand industrial concern. Indeed, threats against IoT devices and services could cause security breaches anddata leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paperstudied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approachcould provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore,highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensivereview of IoT features and design. It mainly focused on intrusion detection based on the machine learningschema and built a taxonomy of different IoT attacks and threats. This paper also compared between thedifferent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusiondetection solutions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">internet of things (iot)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">wireless sensor network (wsn)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning (ml)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">intrusion detection (id)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Abir Kaceam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lamia Chaari</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">EAI Endorsed Transactions on Security and Safety</subfield><subfield code="d">European Alliance for Innovation (EAI), 2016</subfield><subfield code="g">8(2021), 29</subfield><subfield code="w">(DE-627)1685371272</subfield><subfield code="x">20329393</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:29</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.4108/eai.6-10-2021.171246</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/0e738de4a5c64bd08fe69765dcbcfd79</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://eudl.eu/pdf/10.4108/eai.6-10-2021.171246</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2032-9393</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2021</subfield><subfield code="e">29</subfield></datafield></record></collection>
score	7.399646

Nicht das Richtige dabei?

Schreiben Sie uns!

A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?