Novel Deep Learning-Enabled LSTM Autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems

Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security of communications and computing components of AVs can be compromised using advanced hacking techniq...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on intelligent transportation systems Ročník 22; číslo 7; s. 4507 - 4518
Hlavní autori:	Ashraf, Javed, Bakhshi, Asim D., Moustafa, Nour, Khurshid, Hasnat, Javed, Abdullah, Beheshti, Amin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms autoencoder Autonomous vehicles CAN bus Computer architecture Computer crime Computer networks Cybersecurity Datasets Deep learning Denial of service attacks Gateways Intelligent transport systems Intelligent transportation systems Internet of Vehicles Intrusion detection intrusion detection system Intrusion detection systems LSTM Machine learning Security management Short term Spoofing Training Transportation networks
ISSN:	1524-9050, 1558-0016
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security of communications and computing components of AVs can be compromised using advanced hacking techniques, thus barring AVs from the effective use in our routine lives. Once manual vehicles are connected to the Internet, called the Internet of Vehicles (IoVs), it would be exploited by cyber-attacks, like denial of service, sniffing, distributed denial of service, spoofing and replay attacks. In this article, we present a deep learning-based Intrusion Detection System (IDS) for ITS, in particular, to discover suspicious network activity of In-Vehicles Networks (IVN), vehicles to vehicles (V2V) communications and vehicles to infrastructure (V2I) networks. A Deep Learning architecture-based Long-Short Term Memory (LSTM) autoencoder algorithm is designed to recognize intrusive events from the central network gateways of AVs. The proposed IDS is evaluated using two benchmark datasets, i.e., the car hacking dataset for in-vehicle communications and the UNSW-NB15 dataset for external network communications. The experimental results demonstrated that our proposed system achieved over a 99% accuracy for detecting all types of attacks on the car hacking dataset and a 98% accuracy on the UNSW-NB15 dataset, outperforming other eight intrusion detection techniques.
AbstractList	Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security of communications and computing components of AVs can be compromised using advanced hacking techniques, thus barring AVs from the effective use in our routine lives. Once manual vehicles are connected to the Internet, called the Internet of Vehicles (IoVs), it would be exploited by cyber-attacks, like denial of service, sniffing, distributed denial of service, spoofing and replay attacks. In this article, we present a deep learning-based Intrusion Detection System (IDS) for ITS, in particular, to discover suspicious network activity of In-Vehicles Networks (IVN), vehicles to vehicles (V2V) communications and vehicles to infrastructure (V2I) networks. A Deep Learning architecture-based Long-Short Term Memory (LSTM) autoencoder algorithm is designed to recognize intrusive events from the central network gateways of AVs. The proposed IDS is evaluated using two benchmark datasets, i.e., the car hacking dataset for in-vehicle communications and the UNSW-NB15 dataset for external network communications. The experimental results demonstrated that our proposed system achieved over a 99% accuracy for detecting all types of attacks on the car hacking dataset and a 98% accuracy on the UNSW-NB15 dataset, outperforming other eight intrusion detection techniques.
Author	Javed, Abdullah Beheshti, Amin Moustafa, Nour Ashraf, Javed Bakhshi, Asim D. Khurshid, Hasnat
Author_xml	– sequence: 1 givenname: Javed orcidid: 0000-0003-0491-5649 surname: Ashraf fullname: Ashraf, Javed email: javed.ashraf@mcs.edu.pk organization: Department of Computer Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan – sequence: 2 givenname: Asim D. orcidid: 0000-0002-9516-9153 surname: Bakhshi fullname: Bakhshi, Asim D. email: asim.dilawar@mcs.edu.pk organization: Department of Computer Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan – sequence: 3 givenname: Nour orcidid: 0000-0001-6127-9349 surname: Moustafa fullname: Moustafa, Nour email: nour.moustafa@unsw.edu.au organization: School of Engineering and Information Technology, University of New South Wales at ADFA, Canberra, ACT, Australia – sequence: 4 givenname: Hasnat surname: Khurshid fullname: Khurshid, Hasnat email: hasnat@mcs.edu.pk organization: Department of Computer Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan – sequence: 5 givenname: Abdullah surname: Javed fullname: Javed, Abdullah email: abdullahjaved.case@gmail.com organization: Sir Syed Centre for Advanced Studies in Engineering (CASE), Institute of Technology, Islamabad, Pakistan – sequence: 6 givenname: Amin surname: Beheshti fullname: Beheshti, Amin email: amin.beheshti@mq.edu.au organization: Department of Computing, Macquarie University, Sydney, NSW, Australia
BookMark	eNp9kEtPwzAQhC0EEs8fgLhY4pziR5zExwoKVCpwaDhHjrMuRqldbKcSV345qYo4cOC0q9F8s6s5RYfOO0DokpIJpUTe1PN6OWGEkQkntKwqdoBOqBBVRggtDnc7yzNJBDlGpzG-j2ouKD1BX89-Cz2-A9jgBajgrFtlM6faHjq8WNZPeDokD077DgKeBv1mE-g0BMDGB3xnox4DwkjhqfNr1fsh4tkWXIr4Pvg1nrsEfW9Xo4LroFzc-JBUst7h5WdMsI7n6MioPsLFzzxDr_ez-vYxW7w8zG-ni0wzyVMmoJVFS01Rlm3Fu5brrsyV4aqCThipVV4okRuQkjGujATWEi3BUK6VUi3nZ-h6n7sJ_mOAmJp3PwQ3nmyYEJQXTFTF6Cr3Lh18jAFMo-3-3xSU7RtKml3hza7wZld481P4SNI_5CbYtQqf_zJXe8YCwK9fUllJUvFvwbaRgw
CODEN	ITISFG
CitedBy_id	crossref_primary_10_1109_TITS_2021_3105834 crossref_primary_10_3390_electronics12102284 crossref_primary_10_1051_e3sconf_202346900082 crossref_primary_10_1109_ACCESS_2021_3136147 crossref_primary_10_1109_ACCESS_2023_3307473 crossref_primary_10_3390_s22197655 crossref_primary_10_1002_ett_70239 crossref_primary_10_1016_j_adhoc_2025_103770 crossref_primary_10_1016_j_compeleceng_2022_108308 crossref_primary_10_1016_j_sysarc_2025_103567 crossref_primary_10_1016_j_compeleceng_2022_108543 crossref_primary_10_3233_IDT_240231 crossref_primary_10_1145_3540198 crossref_primary_10_1109_TITS_2022_3202869 crossref_primary_10_1109_JIOT_2021_3122021 crossref_primary_10_1109_TITS_2022_3221388 crossref_primary_10_1007_s00521_024_10967_9 crossref_primary_10_1109_TITS_2021_3106378 crossref_primary_10_1016_j_comnet_2024_110768 crossref_primary_10_1016_j_vehcom_2024_100845 crossref_primary_10_1002_itl2_70046 crossref_primary_10_1109_TITS_2025_3567940 crossref_primary_10_1016_j_future_2025_107779 crossref_primary_10_3390_fi16060185 crossref_primary_10_1109_JIOT_2024_3524504 crossref_primary_10_1109_TITS_2024_3478371 crossref_primary_10_1109_TIV_2023_3304762 crossref_primary_10_1109_JIOT_2021_3130156 crossref_primary_10_1002_widm_1515 crossref_primary_10_1109_ACCESS_2024_3386631 crossref_primary_10_1016_j_trc_2023_104058 crossref_primary_10_1007_s13042_024_02401_2 crossref_primary_10_1007_s11704_023_3026_8 crossref_primary_10_1007_s12083_025_01961_6 crossref_primary_10_1109_TIFS_2022_3228493 crossref_primary_10_1109_MCE_2022_3154346 crossref_primary_10_3390_s22041340 crossref_primary_10_3390_iot3020017 crossref_primary_10_1007_s41870_023_01367_8 crossref_primary_10_1016_j_ins_2023_120057 crossref_primary_10_1109_TITS_2024_3351438 crossref_primary_10_3390_s23218788 crossref_primary_10_1109_JAS_2021_1004261 crossref_primary_10_32604_cmc_2023_039583 crossref_primary_10_1016_j_iot_2024_101245 crossref_primary_10_1109_TITS_2024_3524790 crossref_primary_10_1016_j_imavis_2025_105736 crossref_primary_10_1007_s42154_025_00355_x crossref_primary_10_1007_s13369_024_09748_2 crossref_primary_10_3390_app13031252 crossref_primary_10_1109_TITS_2022_3188671 crossref_primary_10_1371_journal_pone_0301897 crossref_primary_10_1109_MCE_2021_3137790 crossref_primary_10_1007_s10922_024_09873_1 crossref_primary_10_1016_j_procs_2024_10_050 crossref_primary_10_1049_itr2_70017 crossref_primary_10_1109_JIOT_2023_3303271 crossref_primary_10_1061_JAEEEZ_ASENG_4576 crossref_primary_10_1109_JIOT_2024_3448505 crossref_primary_10_1016_j_rineng_2024_103171 crossref_primary_10_7717_peerj_cs_1648 crossref_primary_10_1016_j_cose_2022_102919 crossref_primary_10_1177_03611981231159118 crossref_primary_10_1016_j_suscom_2023_100936 crossref_primary_10_3390_app13116369 crossref_primary_10_1007_s40747_022_00705_w crossref_primary_10_1109_TITS_2022_3229517 crossref_primary_10_1109_TITS_2022_3146024 crossref_primary_10_3390_s25134174 crossref_primary_10_1080_19393555_2024_2307638 crossref_primary_10_1016_j_measurement_2023_112884 crossref_primary_10_1016_j_ijcip_2025_100745 crossref_primary_10_1007_s13369_024_08787_z crossref_primary_10_1109_ACCESS_2023_3347619 crossref_primary_10_1016_j_compeleceng_2025_110318 crossref_primary_10_1109_TITS_2024_3509459 crossref_primary_10_1016_j_asoc_2025_113346 crossref_primary_10_1016_j_comnet_2024_110560 crossref_primary_10_1109_ACCESS_2023_3273904 crossref_primary_10_1109_JIOT_2021_3084796 crossref_primary_10_3390_app12125864 crossref_primary_10_1109_TCE_2023_3326384 crossref_primary_10_1109_TGCN_2022_3179350 crossref_primary_10_3390_electronics12183779 crossref_primary_10_1007_s11071_024_10041_y crossref_primary_10_1109_TITS_2024_3380263 crossref_primary_10_1109_TNSE_2024_3397719 crossref_primary_10_1109_ACCESS_2021_3098675 crossref_primary_10_1016_j_knosys_2024_112091 crossref_primary_10_3390_fi17070310 crossref_primary_10_1016_j_psep_2022_06_036 crossref_primary_10_1109_TC_2023_3333253 crossref_primary_10_2514_1_I011474 crossref_primary_10_3390_s23084086 crossref_primary_10_1016_j_engappai_2022_105458 crossref_primary_10_1109_COMST_2023_3236448 crossref_primary_10_1007_s10791_025_09696_x crossref_primary_10_1016_j_cstp_2024_101247 crossref_primary_10_1016_j_adhoc_2024_103590 crossref_primary_10_1007_s10207_023_00807_7 crossref_primary_10_3390_electronics11101529 crossref_primary_10_1007_s12083_024_01627_9 crossref_primary_10_1109_TVT_2024_3385916 crossref_primary_10_1145_3570954 crossref_primary_10_1109_COMST_2023_3309864 crossref_primary_10_3390_electronics14132572 crossref_primary_10_1109_JSEN_2022_3208013 crossref_primary_10_1155_2021_7126913 crossref_primary_10_1109_ACCESS_2025_3569356 crossref_primary_10_1109_MNET_2024_3486220 crossref_primary_10_1016_j_geoen_2024_213279 crossref_primary_10_1007_s11227_024_06269_5 crossref_primary_10_1109_TITS_2021_3130906 crossref_primary_10_1109_TITS_2023_3267462 crossref_primary_10_32604_cmc_2023_033422 crossref_primary_10_1016_j_cose_2022_102842 crossref_primary_10_1007_s10586_025_05124_3 crossref_primary_10_1016_j_procs_2023_11_037 crossref_primary_10_1007_s40860_024_00231_1 crossref_primary_10_1063_5_0283683 crossref_primary_10_1016_j_vehcom_2024_100830 crossref_primary_10_4271_12_08_04_0031 crossref_primary_10_1016_j_phycom_2022_101938 crossref_primary_10_1016_j_knosys_2025_114436 crossref_primary_10_1109_ACCESS_2022_3176317 crossref_primary_10_1016_j_vehcom_2022_100515 crossref_primary_10_3390_s23094399 crossref_primary_10_1063_5_0221722 crossref_primary_10_1109_TITS_2021_3102581 crossref_primary_10_3390_wevj15080356 crossref_primary_10_1007_s12083_023_01508_7 crossref_primary_10_1016_j_knosys_2024_111387
Cites_doi	10.1109/VTCSpring.2018.8417690 10.1201/9781482276596 10.1109/TBDATA.2017.2715166 10.1016/j.array.2019.100013 10.1109/DSAA.2016.20 10.1109/TVT.2019.2893675 10.1007/BF00332918 10.1109/TII.2019.2957140 10.1016/j.suscom.2019.05.002 10.1109/TIFS.2018.2890808 10.1016/0364-0213(90)90002-E 10.1162/neco.1997.9.8.1735 10.1007/978-3-030-36167-9_9 10.1109/TASE.2016.2599436 10.1080/19393555.2015.1125974 10.1109/MNET.001.1900220 10.1109/TNSE.2020.2990984 10.1109/ICC.2016.7510811 10.1109/MWC.001.1900311 10.1016/j.adhoc.2019.02.001 10.1109/MCOM.2018.1701270 10.1109/TVT.2019.2907269 10.1109/MilCIS.2017.8190422 10.1109/GLOBECOM38437.2019.9013892 10.1109/MNET.2017.1600257 10.1109/ACCESS.2018.2825538 10.1371/journal.pone.0155781 10.1016/j.sysarc.2020.101761 10.1109/TITS.2019.2908074 10.1109/TNSM.2020.2972774 10.1109/MilCIS.2015.7348942 10.1007/s12243-016-0551-6 10.1109/PST.2018.8514157 10.1109/MilCIS.2017.8190421 10.1109/PST.2017.00017 10.1016/j.asoc.2018.05.049 10.1109/TVT.2020.2977829 10.1016/j.eswa.2019.04.017
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D
DOI	10.1109/TITS.2020.3017882
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Civil Engineering Abstracts
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-0016
EndPage	4518
ExternalDocumentID	10_1109_TITS_2020_3017882 9198908
Genre	orig-research
GrantInformation_xml	– fundername: 2020 Australian Spitfire Memorial Defence Fund grantid: PS39150
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AIBXA AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS ZY4 AAYXX CITATION 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D
ID	FETCH-LOGICAL-c293t-5eb96b1f677b83db3cd74af3a8ed5f9ca46a54fe99223af9e2b0c9ef13caaab33
IEDL.DBID	RIE
ISICitedReferencesCount	179
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000673518500055&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1524-9050
IngestDate	Sun Nov 30 04:18:32 EST 2025 Sat Nov 29 06:34:55 EST 2025 Tue Nov 18 22:53:28 EST 2025 Wed Aug 27 02:40:52 EDT 2025
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-c293t-5eb96b1f677b83db3cd74af3a8ed5f9ca46a54fe99223af9e2b0c9ef13caaab33
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-9516-9153 0000-0003-0491-5649 0000-0001-6127-9349
PQID	2551362586
PQPubID	75735
PageCount	12
ParticipantIDs	crossref_primary_10_1109_TITS_2020_3017882 proquest_journals_2551362586 crossref_citationtrail_10_1109_TITS_2020_3017882 ieee_primary_9198908
PublicationCentury	2000
PublicationDate	2021-07-01
PublicationDateYYYYMMDD	2021-07-01
PublicationDate_xml	– month: 07 year: 2021 text: 2021-07-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on intelligent transportation systems
PublicationTitleAbbrev	TITS
PublicationYear	2021
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	ref34 ref12 ref37 ref15 miller (ref3) 2015 ref36 ref31 ref30 marsden (ref13) 2017 ref33 ref11 ref32 ref10 bernardini (ref5) 2017; 10 ref2 ref1 ref39 ref17 ref38 ref16 ref19 song (ref35) 2020; 21 ref18 ref24 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref44 ref21 ref43 moustaf (ref14) 2015 ref28 nie (ref4) 2017 ref27 ref29 ref8 ref7 ref9 ref6 ref40
References_xml	– ident: ref33 doi: 10.1109/VTCSpring.2018.8417690 – ident: ref20 doi: 10.1201/9781482276596 – ident: ref21 doi: 10.1109/TBDATA.2017.2715166 – ident: ref8 doi: 10.1016/j.array.2019.100013 – ident: ref32 doi: 10.1109/DSAA.2016.20 – ident: ref26 doi: 10.1109/TVT.2019.2893675 – ident: ref43 doi: 10.1007/BF00332918 – ident: ref15 doi: 10.1109/TII.2019.2957140 – ident: ref37 doi: 10.1016/j.suscom.2019.05.002 – ident: ref22 doi: 10.1109/TIFS.2018.2890808 – ident: ref42 doi: 10.1016/0364-0213(90)90002-E – ident: ref44 doi: 10.1162/neco.1997.9.8.1735 – ident: ref2 doi: 10.1007/978-3-030-36167-9_9 – ident: ref16 doi: 10.1109/TASE.2016.2599436 – ident: ref17 doi: 10.1080/19393555.2015.1125974 – ident: ref1 doi: 10.1109/MNET.001.1900220 – ident: ref36 doi: 10.1109/TNSE.2020.2990984 – ident: ref18 doi: 10.1109/ICC.2016.7510811 – ident: ref39 doi: 10.1109/MWC.001.1900311 – ident: ref34 doi: 10.1016/j.adhoc.2019.02.001 – ident: ref29 doi: 10.1109/MCOM.2018.1701270 – ident: ref27 doi: 10.1109/TVT.2019.2907269 – start-page: 204 year: 2015 ident: ref14 article-title: Creating novel features to anomaly network detection using DARPA-2009 data set publication-title: Proc 14th Eur Conf Cyber Warfare Secur – ident: ref25 doi: 10.1109/MilCIS.2017.8190422 – ident: ref28 doi: 10.1109/GLOBECOM38437.2019.9013892 – ident: ref7 doi: 10.1109/MNET.2017.1600257 – ident: ref30 doi: 10.1109/ACCESS.2018.2825538 – ident: ref11 doi: 10.1371/journal.pone.0155781 – ident: ref41 doi: 10.1016/j.sysarc.2020.101761 – start-page: 1 year: 2017 ident: ref4 publication-title: Free-Fall Hacking Tesla from Wireless to Can Bus – ident: ref6 doi: 10.1109/TITS.2019.2908074 – start-page: 91 year: 2015 ident: ref3 publication-title: Remote exploitation of an unaltered passenger vehicle – ident: ref40 doi: 10.1109/TNSM.2020.2972774 – ident: ref31 doi: 10.1109/MilCIS.2015.7348942 – start-page: 353 year: 2017 ident: ref13 article-title: Probability risk identification based intrusion detection system for SCADA systems publication-title: Proc Int Conf Mobile Netw Manage – ident: ref9 doi: 10.1007/s12243-016-0551-6 – ident: ref10 doi: 10.1109/PST.2018.8514157 – ident: ref23 doi: 10.1109/MilCIS.2017.8190421 – ident: ref12 doi: 10.1109/PST.2017.00017 – ident: ref19 doi: 10.1016/j.asoc.2018.05.049 – ident: ref38 doi: 10.1109/TVT.2020.2977829 – ident: ref24 doi: 10.1016/j.eswa.2019.04.017 – volume: 21 year: 2020 ident: ref35 article-title: In-vehicle network intrusion detection using deep convolutional neural network publication-title: Veh Commun – volume: 10 start-page: 13 year: 2017 ident: ref5 article-title: Security and privacy in vehicular communications: Challenges and opportunities publication-title: Veh Commun
SSID	ssj0014511
Score	2.6674848
Snippet	Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	4507
SubjectTerms	Algorithms autoencoder Autonomous vehicles CAN bus Computer architecture Computer crime Computer networks Cybersecurity Datasets Deep learning Denial of service attacks Gateways Intelligent transport systems Intelligent transportation systems Internet of Vehicles Intrusion detection intrusion detection system Intrusion detection systems LSTM Machine learning Security management Short term Spoofing Training Transportation networks
Title	Novel Deep Learning-Enabled LSTM Autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems
URI	https://ieeexplore.ieee.org/document/9198908 https://www.proquest.com/docview/2551362586
Volume	22
WOSCitedRecordID	wos000673518500055&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 Electronic Library (IEL) customDbUrl: eissn: 1558-0016 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014511 issn: 1524-9050 databaseCode: RIE dateStart: 20000101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Na9tAEF0S00NzaJKmoc4Xe8ipdGtpd6XVHk1sU4NrAnFLbmI_RsXgWMGS_QPyy7O7lkVCQ6A3HXZA8EY7M5o3bxC6Bm5dVp8Z4oKxINwmkkiXCBNgmicMEqqCpNCfiZhOs_t7ebuHvrezMAAQyGfwwz-GXr4tzdr_KutJT_Dxk737QojtrFbbMfA6W0EblXIio2TXwYwj2ZuNZ3euEqSuQPXL6DP6KgaFpSr_3MQhvIwO_-_FjtCnJo3E_S3ux2gPlp_RwQtxwRP0NC03sMADgEfcqKj-JcMwKmXx5G72C_fXdel1LC2scP9FQwG7RBYP5pXx9E5nhfvL8kEtynWFh54eWeHRqnzA41bNs8atRnoAGjc66F_Q79FwdvOTNBsXiHFhvyYJaJnquEiF0BmzmhkruCqYysAmhTSKpyrhBXgxW6YKCVRHRkIRM6OU0oydos6yXMJXhOOCs1RR5wrKcBtD5jxCO0NNIwEuyeuiaIdBbho5cr8VY5GHsiSSuYct97DlDWxd9K01edxqcbx3-MTj1B5sIOqiix3QefO1Vjn1W25cIZilZ29bnaOP1HNZAk33AnXq1Rou0QezqefV6io44jPP5t2e
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1La9tAEB5CWmhz6CsNcZK2e8gpdBtpd_XYo0lsYuqYQtSSm9jHKAQcK1hyfkB-eXbXskhpKfSmww4IvtHOjOabbwCOUViX1eeGumCcUWETSaVLhClyLRKOCVNBUujXNJvN8utr-WMLvvazMIgYyGf4zT-GXr6tzcr_KjuVnuDjJ3tfJEKweD2t1fcMvNJWUEdlgsoo2fQw40ieFpPiytWCzJWofh19zn6LQmGtyh93cQgw47f_92rv4E2XSJLhGvn3sIWLD7DzTF5wFx5n9QPOyTniPel0VG_oKAxLWTK9Ki7JcNXWXsnS4pIMn7UUiEtlyfltYzzB01mR4aK-U_N61ZCRJ0g2ZLys78ik1_NsSa-SHqAmnRL6R_g5HhVnF7TbuUCNC_wtTVDLVMdVmmU651ZzYzOhKq5ytEkljRKpSkSFXs6Wq0oi05GRWMXcKKU053uwvagXuA8krgRPFXPOoIywMebOJ7Qz1CzK0KV5A4g2GJSmEyT3ezHmZShMIll62EoPW9nBNoCT3uR-rcbxr8O7Hqf-YAfRAI42QJfd99qUzO-5caVgnh783eoLvLooLqfldDL7fgivmWe2BNLuEWy3yxV-gpfmob1tlp-DUz4BeMXg5Q
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=Novel+Deep+Learning-Enabled+LSTM+Autoencoder+Architecture+for+Discovering+Anomalous+Events+From+Intelligent+Transportation+Systems&rft.jtitle=IEEE+transactions+on+intelligent+transportation+systems&rft.au=Ashraf%2C+Javed&rft.au=Bakhshi%2C+Asim+D.&rft.au=Moustafa%2C+Nour&rft.au=Khurshid%2C+Hasnat&rft.date=2021-07-01&rft.pub=IEEE&rft.issn=1524-9050&rft.volume=22&rft.issue=7&rft.spage=4507&rft.epage=4518&rft_id=info:doi/10.1109%2FTITS.2020.3017882&rft.externalDocID=9198908
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1524-9050&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1524-9050&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1524-9050&client=summon