Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM ena...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Journal of lightwave technology Ročník 37; číslo 7; s. 1742 - 1749
Hlavní autori:	Chen, Xiaoliang, Li, Baojia, Proietti, Roberto, Zhu, Zuqing, Yoo, S. J. Ben
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Anomalies Anomaly detection Artificial intelligence Artificial neural networks Clustering Clustering algorithms Cognition data clustering module Data clustering module (DCM) data regression and classification module data regression and classification module (DRCM) DCM DRCM ENGINEERING hybrid unsupervised and supervised machine learning Knowledge engineering Knowledge management Machine learning Modules Monitoring Optical communication Optical fiber networks Regression analysis self-taught anomaly detection
ISSN:	0733-8724, 1558-2213
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to <inline-formula><tex-math notation="LaTeX">99\%</tex-math></inline-formula> anomaly detection accuracy can be achieved with a false positive rate below <inline-formula><tex-math notation="LaTeX">1\%</tex-math></inline-formula> .
AbstractList	This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to [Formula Omitted] anomaly detection accuracy can be achieved with a false positive rate below [Formula Omitted] . This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to <inline-formula><tex-math notation="LaTeX">99\%</tex-math></inline-formula> anomaly detection accuracy can be achieved with a false positive rate below <inline-formula><tex-math notation="LaTeX">1\%</tex-math></inline-formula> . Here this paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.
Author	Chen, Xiaoliang Proietti, Roberto Yoo, S. J. Ben Li, Baojia Zhu, Zuqing
Author_xml	– sequence: 1 givenname: Xiaoliang orcidid: 0000-0002-7805-6237 surname: Chen fullname: Chen, Xiaoliang email: xlichen@ucdavis.edu organization: Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA – sequence: 2 givenname: Baojia surname: Li fullname: Li, Baojia email: libaojia@mail.ustc.edu.cn organization: School of Information Science and Technology, University of Science and Technology of China, Hefei, China – sequence: 3 givenname: Roberto surname: Proietti fullname: Proietti, Roberto email: rproietti@ucdavis.edu organization: Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA – sequence: 4 givenname: Zuqing orcidid: 0000-0002-4251-788X surname: Zhu fullname: Zhu, Zuqing email: zqzhu@ieee.org organization: School of Information Science and Technology, University of Science and Technology of China, Hefei, China – sequence: 5 givenname: S. J. Ben surname: Yoo fullname: Yoo, S. J. Ben email: sbyoo@ucdavis.edu organization: Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA
BackLink	https://www.osti.gov/servlets/purl/1612677$$D View this record in Osti.gov
BookMark	eNp9kL9vGyEYhlGVSnXc7JW6oHY-hx8Hx41RmsapnGSwrY6Iw9_ZpBdwATfyfx8sWxk6RAwfw_PC9z7n6MwHDwh9oWRCKWkvf80WE0ZoO2EtYbVqPqARFUJVjFF-hkak4bxSDas_ofOUngihdYFGqJ_D0FcLs1tvMr7y4dkMe_wDMtjsgse_Xd7g6b6LboWXPu22EP-5BKvL-dsV3xu7cR7wDEz0zq-x8_hxm501A36A_BLin_QZfezNkODiNMdo-fNmcT2tZo-3d9dXs8pySXKl2p4As20vRMdWsuubWhrbSW46Q4zgsmc1gCk9OmIMUyuurKBNI4ngtKM1H6Nvx3dDyk4n60qRjQ3elz6aSspk0xTo-xHaxvB3Bynrp7CLvuylGSOct0ooWihypGwMKUXo9Ta6ZxP3mhJ9UK6Lcn1Qrk_KS0T-FykLmIPIHI0b3gt-PQYdALz9o6Qop-WvpZyQSw
CODEN	JLTEDG
CitedBy_id	crossref_primary_10_1016_j_ijepes_2025_110718 crossref_primary_10_1038_s41598_021_02583_7 crossref_primary_10_1109_LCOMM_2021_3055064 crossref_primary_10_1109_TNSM_2020_3039938 crossref_primary_10_1364_JOCN_502937 crossref_primary_10_1364_JOCN_527874 crossref_primary_10_1109_JLT_2022_3160101 crossref_primary_10_1109_JLT_2022_3172523 crossref_primary_10_1364_JOCN_402884 crossref_primary_10_1007_s11432_022_3557_9 crossref_primary_10_1109_JLT_2020_2987032 crossref_primary_10_1109_JSTQE_2022_3156157 crossref_primary_10_1364_JOCN_12_00A113 crossref_primary_10_1109_ACCESS_2025_3577968 crossref_primary_10_2478_amns_2024_2222 crossref_primary_10_1109_JLT_2022_3168594 crossref_primary_10_1364_JOCN_423667 crossref_primary_10_1109_JLT_2020_3034135 crossref_primary_10_1515_nanoph_2021_0578 crossref_primary_10_1109_ACCESS_2021_3111092 crossref_primary_10_1007_s11432_020_2874_y crossref_primary_10_1109_JLT_2022_3227719 crossref_primary_10_1364_JOCN_482932 crossref_primary_10_1109_ACCESS_2024_3404866 crossref_primary_10_1109_JLT_2019_2922586 crossref_primary_10_1364_JOCN_457687 crossref_primary_10_1109_ACCESS_2024_3498704 crossref_primary_10_1109_JLT_2020_3003905 crossref_primary_10_1109_JSTQE_2022_3151878 crossref_primary_10_1007_s10489_020_02075_7 crossref_primary_10_1109_JLT_2021_3134098 crossref_primary_10_4218_etrij_2021_0182 crossref_primary_10_1515_joc_2022_0216 crossref_primary_10_3390_electronics12030601 crossref_primary_10_1016_j_jnca_2020_102576 crossref_primary_10_1109_JLT_2019_2923558 crossref_primary_10_1109_TNSM_2022_3198139 crossref_primary_10_1364_JOCN_409817 crossref_primary_10_1109_JLT_2020_2989153 crossref_primary_10_1109_ACCESS_2021_3083060 crossref_primary_10_3390_s24082636 crossref_primary_10_1364_JOCN_12_000A18 crossref_primary_10_1145_3530812 crossref_primary_10_1049_2024_8821891 crossref_primary_10_1109_JLT_2021_3055850 crossref_primary_10_1364_JOCN_439801 crossref_primary_10_1364_JOCN_484577 crossref_primary_10_1109_JLT_2020_3007491 crossref_primary_10_1109_JLT_2021_3125974 crossref_primary_10_1109_JLT_2022_3150362 crossref_primary_10_3390_ai6070131 crossref_primary_10_1109_JLT_2019_2947562 crossref_primary_10_1109_JSAC_2021_3064657
Cites_doi	10.1109/ECOC.2018.8535155 10.1109/MNET.2004.1276610 10.1364/OFC.2018.Th4D.7 10.1109/JLT.2018.2795345 10.1109/TKDE.2010.235 10.1364/JOCN.10.000162 10.1364/JOCN.9.0000A9 10.1109/COMST.2018.2880039 10.1364/JOCN.5.000836 10.1364/OFC.2018.M3A.5 10.1109/COMST.2014.2336610 10.1109/JLT.2013.2296781 10.1109/JSAC.2005.852182 10.1109/MCOM.2006.248169 10.1109/JLT.2017.2747223 10.1364/OFC.2019.M2A.2 10.2307/2346830 10.1109/JLT.2013.2294389 10.1109/JLT.2017.2660540 10.1109/MNET.2015.7293303 10.1364/JOCN.10.000A27 10.1364/JOCN.11.0000A1 10.1364/JOCN.10.000D29 10.1109/ECOC.2018.8535185 10.1109/INFOCOM.2014.6847918 10.1364/JOCN.10.000344 10.1364/OFC.2018.W4F.2 10.1109/JLT.2015.2456152 10.1109/JLT.2018.2864676 10.1109/MCOM.2018.1701191 10.1109/JLT.2015.2456052 10.1109/JLT.2017.2781540 10.1109/JLT.2012.2227683 10.1109/ECOC.2017.8346218
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019
CorporateAuthor	Univ. of California, Davis, CA (United States)
CorporateAuthor_xml	– name: Univ. of California, Davis, CA (United States)
DBID	97E RIA RIE AAYXX CITATION 7SP 7U5 8FD H8D L7M OIOZB OTOTI
DOI	10.1109/JLT.2019.2902487
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Solid State and Superconductivity Abstracts Technology Research Database Aerospace Database Advanced Technologies Database with Aerospace OSTI.GOV - Hybrid OSTI.GOV
DatabaseTitle	CrossRef Aerospace Database Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Aerospace Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Applied Sciences Physics Engineering
EISSN	1558-2213
EndPage	1749
ExternalDocumentID	1612677 10_1109_JLT_2019_2902487 8656569
Genre	orig-research
GrantInformation_xml	– fundername: U.S. Department of Energy grantid: DE-SC0016700 funderid: 10.13039/100000015 – fundername: National Science Foundation ICE-T:RC grantid: 1836921
GroupedDBID	-~X 0R~ 29K 4.4 5GY 6IK 85S 8SL 97E AAJGR AARMG AASAJ AAWJZ AAWTH ABAZT ABQJQ ABVLG ACBEA ACGFO ACGFS ACIWK AEDJG AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATHME ATWAV AYPRP AZSQR BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 D-I DSZJF DU5 EBS EJD HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL OFLFD OPJBK P2P RIA RIE RNS ROL ROS TN5 TR6 ZCA AAYXX CITATION 7SP 7U5 8FD H8D L7M RIG OIOZB OTOTI ROP
ID	FETCH-LOGICAL-c360t-89f0e2c9f55b2d6bf746acb63aba0a536f24eea872b0aa28d38c517760531b143
IEDL.DBID	RIE
ISICitedReferencesCount	74
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000464133000012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0733-8724
IngestDate	Mon Feb 26 05:26:08 EST 2024 Mon Jun 30 10:19:31 EDT 2025 Sat Nov 29 02:11:21 EST 2025 Tue Nov 18 22:33:11 EST 2025 Wed Aug 27 02:44:23 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	7
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c360t-89f0e2c9f55b2d6bf746acb63aba0a536f24eea872b0aa28d38c517760531b143
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 USDOE Office of Science (SC) National Science Foundation (NSF) SC0016700; ICE-T:RC 1836921
ORCID	0000-0002-7805-6237 0000-0002-4251-788X 0000000278056237 000000024251788X
OpenAccessLink	https://www.osti.gov/servlets/purl/1612677
PQID	2203398581
PQPubID	85485
PageCount	8
ParticipantIDs	crossref_primary_10_1109_JLT_2019_2902487 proquest_journals_2203398581 ieee_primary_8656569 osti_scitechconnect_1612677 crossref_citationtrail_10_1109_JLT_2019_2902487
PublicationCentury	2000
PublicationDate	2019-04-01
PublicationDateYYYYMMDD	2019-04-01
PublicationDate_xml	– month: 04 year: 2019 text: 2019-04-01 day: 01
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: United States
PublicationTitle	Journal of lightwave technology
PublicationTitleAbbrev	JLT
PublicationYear	2019
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref35 ref13 ref34 ref12 ref15 ref36 ref14 ref31 ref11 ref32 ref10 ref2 ref1 ref17 ref16 ref19 ref18 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ester (ref30) 0 pedregosa (ref33) 2011; 12 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5
References_xml	– ident: ref26 doi: 10.1109/ECOC.2018.8535155 – ident: ref1 doi: 10.1109/MNET.2004.1276610 – ident: ref35 doi: 10.1364/OFC.2018.Th4D.7 – ident: ref29 doi: 10.1109/JLT.2018.2795345 – ident: ref27 doi: 10.1109/TKDE.2010.235 – ident: ref32 doi: 10.1364/JOCN.10.000162 – ident: ref14 doi: 10.1364/JOCN.9.0000A9 – ident: ref15 doi: 10.1109/COMST.2018.2880039 – ident: ref8 doi: 10.1364/JOCN.5.000836 – ident: ref24 doi: 10.1364/OFC.2018.M3A.5 – ident: ref31 doi: 10.1109/COMST.2014.2336610 – ident: ref13 doi: 10.1109/JLT.2013.2296781 – ident: ref3 doi: 10.1109/JSAC.2005.852182 – ident: ref4 doi: 10.1109/MCOM.2006.248169 – ident: ref22 doi: 10.1109/JLT.2017.2747223 – start-page: 226 year: 0 ident: ref30 article-title: A density-based algorithm for discovering clusters in large spatial databases with noise publication-title: Proc Int'l Conf Knowledge Discovery and Data Mining – ident: ref36 doi: 10.1364/OFC.2019.M2A.2 – ident: ref34 doi: 10.2307/2346830 – ident: ref9 doi: 10.1109/JLT.2013.2294389 – ident: ref17 doi: 10.1109/JLT.2017.2660540 – ident: ref2 doi: 10.1109/MNET.2015.7293303 – ident: ref23 doi: 10.1364/JOCN.10.000A27 – ident: ref11 doi: 10.1364/JOCN.11.0000A1 – ident: ref19 doi: 10.1364/JOCN.10.000D29 – ident: ref28 doi: 10.1109/ECOC.2018.8535185 – ident: ref10 doi: 10.1109/INFOCOM.2014.6847918 – ident: ref7 doi: 10.1364/JOCN.10.000344 – ident: ref21 doi: 10.1364/OFC.2018.W4F.2 – ident: ref5 doi: 10.1109/JLT.2015.2456152 – ident: ref20 doi: 10.1109/JLT.2018.2864676 – ident: ref16 doi: 10.1109/MCOM.2018.1701191 – ident: ref6 doi: 10.1109/JLT.2015.2456052 – ident: ref25 doi: 10.1109/JLT.2017.2781540 – ident: ref12 doi: 10.1109/JLT.2012.2227683 – ident: ref18 doi: 10.1109/ECOC.2017.8346218 – volume: 12 start-page: 2825 year: 2011 ident: ref33 article-title: Scikit-learn: Machine learning in python publication-title: J Mach Learn Res
SSID	ssj0014487
Score	2.5822265
Snippet	This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised... Here this paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and...
SourceID	osti proquest crossref ieee
SourceType	Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	1742
SubjectTerms	Algorithms Anomalies Anomaly detection Artificial intelligence Artificial neural networks Clustering Clustering algorithms Cognition data clustering module Data clustering module (DCM) data regression and classification module data regression and classification module (DRCM) DCM DRCM ENGINEERING hybrid unsupervised and supervised machine learning Knowledge engineering Knowledge management Machine learning Modules Monitoring Optical communication Optical fiber networks Regression analysis self-taught anomaly detection
Title	Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks
URI	https://ieeexplore.ieee.org/document/8656569 https://www.proquest.com/docview/2203398581 https://www.osti.gov/servlets/purl/1612677
Volume	37
WOSCitedRecordID	wos000464133000012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE/IET Electronic Library (IEL) (UW System Shared) customDbUrl: eissn: 1558-2213 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014487 issn: 0733-8724 databaseCode: RIE dateStart: 19830101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3faxQxEB7aouCL1VbxbJU8-CK4vb1kNz8eRS1F6in0in0LSXaiB-fe0d0T-t-bZHMLogg-bR6S3SVfZjKT-WYC8KqS1nnm60LWCouKoy8Us7OCWoOOStkIlxKFL8V8Lm9u1Jc9eDPmwiBiIp_hWWymWH6zdtt4VDaVPJofah_2heBDrtYYMQhuRkqNFowFCafVLiRZqunHy0XkcKkzqmIFL_HbFpTuVAmPdZCoP_Rx2mTOD__v9x7Bw2xMkrcD-o9hD9sjOMyGJcli2x3B_cTzdN0x-Ctc-WJhtsElJ8H1_2FWd-Q99omQ1ZKvy_47ubiLWVzkuu22m6hJOmymV2OTfEr0SyS5Mus3smzJ5006EifzgVTePYHr8w-LdxdFvmqhcIyXfSGVL5E65eva0oZbLypunOXMWFOamnFPK0QTJtaWxlDZMOnqWUAhyrANNtdTOGjXLT4DIlXpZdADhjtaCWZNI2oTqxJ6ayvR8AlMd7OvXa5DHq_DWOnkj5RKB7x0xEtnvCbwehyxGWpw_KPvcQRm7JcxmcBJBFgHsyLWxnWRROR6HcxdykUYdLrDXWcR7jSlJWNK1nL2_O_vPIEH8csDjecUDvrbLb6Ae-5nv-xuX6bV-Qso1uKB
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LbxMxEB6VFkQvLbRFhD7wgQsS22zsXT-OFVClkAakpqI3y_baEClsou6mUv89tuOshECVelof7JXlz2PPeL6ZAXhXcG0ccWXGS2GzglqXCaIHGdbKGsx5xUwMFB6x8Zjf3IjvG_Chi4Wx1kbymT0NzejLr-ZmGZ7K-pwG9UM8ga1QOStFa3U-A29oxOBoRoiXcVysnZK56H8ZTQKLS5xiEXJ4sb8uoVhVxX_mXqb-OZHjNXO--7gJvoCdpE6isxX-L2HD1nuwm1RLlAS32YNnkelpmn1wV3bmsolaeqMceeP_t5rdo0-2jZSsGv2Ytr_Q8D7EcaHrulkuwlnS2Kp_1TXRZSRgWpRys_5E0xp9W8RHcTRe0cqbA7g-_zz5OMxSsYXMEJq3GRcut9gIV5YaV1Q7VlBlNCVKq1yVhDpcWKv8wupcKcwrwk05YIwGKdZe63oFm_W8tq8BcZE77k8CRQ0uGNGqYqUKeQmd1gWraA_669WXJmUiDwUxZjJaJLmQHi8Z8JIJrx6870YsVlk4Hui7H4Dp-iVMenAYAJZesQjZcU2gEZlWeoUXU-YHHa1xl0mIG4lxTojgJR-8-f8_38Lz4eRyJEcX46-HsB1msSL1HMFme7u0x_DU3LXT5vYk7tQ_G9Hlyg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Self-Taught+Anomaly+Detection+With+Hybrid+Unsupervised%2FSupervised+Machine+Learning+in+Optical+Networks&rft.jtitle=Journal+of+lightwave+technology&rft.au=Chen%2C+Xiaoliang&rft.au=Li%2C+Baojia&rft.au=Proietti%2C+Roberto&rft.au=Zhu%2C+Zuqing&rft.date=2019-04-01&rft.issn=0733-8724&rft.eissn=1558-2213&rft.volume=37&rft.issue=7&rft.spage=1742&rft.epage=1749&rft_id=info:doi/10.1109%2FJLT.2019.2902487&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_JLT_2019_2902487
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0733-8724&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0733-8724&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0733-8724&client=summon