Optimized deep autoencoder and BiLSTM for intrusion detection in IoTs-Fog computing

Our world is rapidly evolving toward the Internet of Things (IoT), that connects all gadgets to digital services and simplifies our lives. As IoT devices expand, network vulnerabilities may rise, leading to more network threats. An intrusion detection system (IDS) with low latency and high accuracy...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Multimedia tools and applications Ročník 84; číslo 8; s. 4907 - 4943
Hlavní autor:	Alqahtani, Abdullah
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.03.2025 Springer Nature B.V
Témata:	Accuracy Algorithms Computer Communication Networks Computer Science Computing time Data Structures and Information Theory Edge computing Error detection Internet of Things Intrusion detection systems Machine learning Multimedia Information Systems Network latency Optimization models Special Purpose and Application-Based Systems Workload Deep learning Multiple attack detection Fog computing Sparrow Search Optimization Algorithm Intrusion detection system Bidirectional LSTM Internet of Things Deep autoencoder Hyperparameter tuning
ISSN:	1573-7721, 1380-7501, 1573-7721
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Our world is rapidly evolving toward the Internet of Things (IoT), that connects all gadgets to digital services and simplifies our lives. As IoT devices expand, network vulnerabilities may rise, leading to more network threats. An intrusion detection system (IDS) with low latency and high accuracy is essential in this dynamic IoT environment to identify threats. Many IDSs utilize deep learning (DL) methods, but adjusting parameters for different environments can be challenging. This work proposes an improved hybrid Deep autoencoder (DAE) and Bidirectional long short-term memory (BiLSTM) for fog item installation to protect vital networks from prompt and efficient malicious threat detection. Determining the hyperparameters of complicated DL networks is tough. Low accuracy, efficiency, and performance in high-dimensional models afflict existing approaches. Therefore, the Sparrow Search Optimization Algorithm (SSOA) is adopted to enhance model hyperparameters. Employing IoT-based data, we assess the effectiveness of our suggested model. The outcome of the experiment obtained by analyzing the suggested IDS utilizing CICIDS2017 and BoT-IoT datasets attested to their supremacy over modern systems that are currently available in terms of precision, accuracy, FAR, error rate, and detection rate (DR). To learn more about how well our model works, added two additional metrics: Cohen's Kappa coefficients and Mathew correlation. The proposed optimization model demonstrated its ability to work well with various platforms, adapt to changing requirements, and handle larger workloads. In addition, it is possible to enhance the intrusion detection rate by minimizing both the error rate and computational time. Based on the experiments conducted, the proposed method yields superior results in terms of detection accuracy and reduced latency.
AbstractList	Our world is rapidly evolving toward the Internet of Things (IoT), that connects all gadgets to digital services and simplifies our lives. As IoT devices expand, network vulnerabilities may rise, leading to more network threats. An intrusion detection system (IDS) with low latency and high accuracy is essential in this dynamic IoT environment to identify threats. Many IDSs utilize deep learning (DL) methods, but adjusting parameters for different environments can be challenging. This work proposes an improved hybrid Deep autoencoder (DAE) and Bidirectional long short-term memory (BiLSTM) for fog item installation to protect vital networks from prompt and efficient malicious threat detection. Determining the hyperparameters of complicated DL networks is tough. Low accuracy, efficiency, and performance in high-dimensional models afflict existing approaches. Therefore, the Sparrow Search Optimization Algorithm (SSOA) is adopted to enhance model hyperparameters. Employing IoT-based data, we assess the effectiveness of our suggested model. The outcome of the experiment obtained by analyzing the suggested IDS utilizing CICIDS2017 and BoT-IoT datasets attested to their supremacy over modern systems that are currently available in terms of precision, accuracy, FAR, error rate, and detection rate (DR). To learn more about how well our model works, added two additional metrics: Cohen's Kappa coefficients and Mathew correlation. The proposed optimization model demonstrated its ability to work well with various platforms, adapt to changing requirements, and handle larger workloads. In addition, it is possible to enhance the intrusion detection rate by minimizing both the error rate and computational time. Based on the experiments conducted, the proposed method yields superior results in terms of detection accuracy and reduced latency.
Author	Alqahtani, Abdullah
Author_xml	– sequence: 1 givenname: Abdullah surname: Alqahtani fullname: Alqahtani, Abdullah email: aq.alqahtani@psau.edu.sa organization: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University
BookMark	eNp9kDFPwzAQhS0EEm3hDzBZYg6c7cRJRqgoVCrq0DJbjuNUrlo72M4Av56EIIEYOt0b3nd3703RuXVWI3RD4I4A5PeBEEhpAjRNSFGSMoEzNCFZzpI8p-T8j75E0xD2AIRnNJ2gzbqN5mg-dY1rrVssu-i0Va7WHktb40ez2mxfceM8Njb6Lhhne2fUKg7KWLx025As3A4rd2y7aOzuCl008hD09c-cobfF03b-kqzWz8v5wypRlKWQ1LzWUJa5pLypKlAAvKa8KHnNpIKMpYxQxYmqqibjGQeVZxVkWipJJKNcsxm6Hfe23r13OkSxd523_UnBSDFEBFL0Ljq6lHcheN2I1puj9B-CgBjKE2N5oi9PfJcnoIeKf5AyUQ6Ro5fmcBplIxr6O3an_e9XJ6gvl1uE_w
CitedBy_id	crossref_primary_10_1109_ACCESS_2025_3531659 crossref_primary_10_1371_journal_pone_0323954 crossref_primary_10_1007_s10586_025_05234_y
Cites_doi	10.1007/s11042-022-13836-6 10.1016/j.patcog.2016.06.008 10.1016/j.future.2019.05.041 10.3390/electronics11193007 10.1007/s11042-021-11271-7 10.1016/j.future.2021.09.027 10.1109/JIOT.2020.3002255 10.1016/j.compeleceng.2022.107694 10.1109/TMC.2020.3026580 10.1016/j.procs.2020.03.271 10.1109/ACCESS.2020.3022855 10.1016/j.knosys.2022.108505 10.1007/s10489-019-01436-1 10.1007/s11042-023-16436-0 10.1007/s11042-021-11747-6 10.1016/j.compeleceng.2022.108214 10.3390/sym13040557 10.1109/ACCESS.2020.2988854 10.1155/2023/7107663 10.1007/s12065-019-00291-w 10.3390/electronics10091077 10.1109/ACCESS.2020.2988055 10.1007/s00779-019-01332-y 10.1109/ACCESS.2021.3137201 10.3390/sym12101695 10.1007/s11277-022-09548-7 10.1007/s11042-021-10640-6 10.1016/j.future.2020.07.020 10.1007/s11042-021-11740-z 10.1002/ett.3868 10.1016/j.comnet.2023.109662 10.1007/s11042-023-17677-9 10.3390/s21217016 10.1002/ett.3803 10.1155/2023/1982173 10.1109/ACCESS.2020.3009843 10.1109/ACCESS.2022.3206425 10.1080/21642583.2019.1708830 10.1007/s11042-021-11423-9 10.32604/cmc.2022.031303 10.1109/ACCESS.2019.2895334 10.1016/j.knosys.2022.109364 10.1007/s11042-022-12330-3 10.3390/electronics9111771 10.3390/sym14010161 10.1016/j.cose.2020.101752 10.1007/s12652-020-02696-3 10.1016/j.imu.2020.100368 10.3390/s22218417
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Copyright Springer Nature B.V. Mar 2025
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: Copyright Springer Nature B.V. Mar 2025
DBID	AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D
DOI	10.1007/s11042-024-18919-0
DatabaseName	CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional
DatabaseTitleList	Computer and Information Systems Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1573-7721
EndPage	4943
ExternalDocumentID	10_1007_s11042_024_18919_0
GrantInformation_xml	– fundername: Prince Sattam bin Abdulaziz University grantid: PSAU/2023/R/1444 funderid: http://dx.doi.org/10.13039/100009392
GroupedDBID	-Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29M 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3EH 4.4 406 408 409 40D 40E 5QI 5VS 67Z 6NX 7WY 8AO 8FE 8FG 8FL 8G5 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBE ABDZT ABECU ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACSNA ACZOJ ADHHG ADHIR ADHKG ADIMF ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYFIA AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ7 GQ8 GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITG ITH ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M2O M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PHGZT PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TH9 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 ZMTXR ~EX AAYXX ABBRH ABFSG ABRTQ ACSTC ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR CITATION PHGZM PQGLB 7SC 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c2340-d6de0997a26fbb0c006d26896d3ac0534312c61cbbf56560c75b05eaca1a326e3
IEDL.DBID	RSV
ISSN	1573-7721 1380-7501
IngestDate	Wed Nov 05 15:23:55 EST 2025 Sat Nov 29 08:05:19 EST 2025 Tue Nov 18 22:06:59 EST 2025 Sat Mar 22 01:17:02 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	8
Keywords	Deep learning Multiple attack detection Fog computing Sparrow Search Optimization Algorithm Intrusion detection system Bidirectional LSTM Internet of Things Deep autoencoder Hyperparameter tuning
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c2340-d6de0997a26fbb0c006d26896d3ac0534312c61cbbf56560c75b05eaca1a326e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
PQID	3180016018
PQPubID	54626
PageCount	37
ParticipantIDs	proquest_journals_3180016018 crossref_primary_10_1007_s11042_024_18919_0 crossref_citationtrail_10_1007_s11042_024_18919_0 springer_journals_10_1007_s11042_024_18919_0
PublicationCentury	2000
PublicationDate	20250300
PublicationDateYYYYMMDD	2025-03-01
PublicationDate_xml	– month: 3 year: 2025 text: 20250300
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: Dordrecht
PublicationSubtitle	An International Journal
PublicationTitle	Multimedia tools and applications
PublicationTitleAbbrev	Multimed Tools Appl
PublicationYear	2025
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	R Chowdhury (18919_CR7) 2022; 81 M Zeeshan (18919_CR36) 2021; 10 WM Oboya (18919_CR48) 2023; 11 18919_CR54 18919_CR4 MA Hamza (18919_CR25) 2022; 73 IV Pustokhina (18919_CR5) 2022; 81 S Roy (18919_CR17) 2022; 127 M Shafiq (18919_CR41) 2020; 8 KG Lore (18919_CR44) 2017; 61 AM Banaamah (18919_CR29) 2022; 22 I Babić (18919_CR52) 2021; 13 A Samy (18919_CR2) 2020; 8 18919_CR50 PLS Jayalaxmi (18919_CR53) 2022; 102 M Rani (18919_CR1) 2022; 81 NF Syed (18919_CR26) 2023; 225 P Kumar (18919_CR35) 2021; 12 MA Khan (18919_CR18) 2021; 21 A Sarkar (18919_CR23) 2023; 15 BA NG (18919_CR51) 2020; 113 R Gopi (18919_CR12) 2021; 81 18919_CR28 MA Khan (18919_CR19) 2020; 9 MS Sofla (18919_CR10) 2022; 81 C Tang (18919_CR21) 2020; 12 18919_CR24 Y Otoum (18919_CR31) 2022; 33 A Boukhalfa (18919_CR49) 2020; 10 Y Chen (18919_CR47) 2022; 244 F Hosseini (18919_CR27) 2023; 82 Y Labiod (18919_CR32) 2022; 125 N Koroniotis (18919_CR42) 2019; 100 J Pacheco (18919_CR39) 2020; 8 V Kumar (18919_CR16) 2021; 14 YN Kunang (18919_CR22) 2021; 58 N Kaja (18919_CR6) 2019; 49 A Halbouni (18919_CR46) 2022; 10 M Ijaz (18919_CR11) 2021; 10 S Liao (18919_CR13) 2020; 21 HB Hassen (18919_CR9) 2020; 20 SD Kebede (18919_CR15) 2022; 81 OA Alzubi (18919_CR55) 2022; 11 MP Ramkumar (18919_CR30) 2022; 252 DK Reddy (18919_CR34) 2021; 60 D Stiawan (18919_CR40) 2020; 8 J Xue (18919_CR45) 2020; 8 A-E Benrazek (18919_CR14) 2020; 31 CA De Souza (18919_CR33) 2022; 98 A Samy (18919_CR38) 2020; 8 M Dua (18919_CR43) 2020; 167 SM Kasongo (18919_CR8) 2020; 92 18919_CR3 K Sadaf (18919_CR37) 2020; 8 J Lee (18919_CR20) 2021; 25
References_xml	– volume: 82 start-page: 13369 issue: 9 year: 2023 ident: 18919_CR27 publication-title: Multimed Tools Applic doi: 10.1007/s11042-022-13836-6 – volume: 61 start-page: 650 year: 2017 ident: 18919_CR44 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2016.06.008 – volume: 100 start-page: 779 year: 2019 ident: 18919_CR42 publication-title: Futur Gener Comput Syst doi: 10.1016/j.future.2019.05.041 – volume: 11 start-page: 3007 issue: 19 year: 2022 ident: 18919_CR55 publication-title: Electronics doi: 10.3390/electronics11193007 – volume: 10 start-page: 3315 issue: 3 year: 2020 ident: 18919_CR49 publication-title: Int J Electr Comput Eng – volume: 81 start-page: 34951 issue: 24 year: 2022 ident: 18919_CR5 publication-title: Multimed Tools Applic doi: 10.1007/s11042-021-11271-7 – volume: 127 start-page: 276 year: 2022 ident: 18919_CR17 publication-title: Futur Gener Comput Syst doi: 10.1016/j.future.2021.09.027 – volume: 8 start-page: 3242 issue: 5 year: 2020 ident: 18919_CR41 publication-title: IEEE Internet Things J doi: 10.1109/JIOT.2020.3002255 – volume: 98 start-page: 107694 year: 2022 ident: 18919_CR33 publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2022.107694 – volume: 21 start-page: 1596 issue: 5 year: 2020 ident: 18919_CR13 publication-title: IEEE Trans Mob Comput doi: 10.1109/TMC.2020.3026580 – volume: 167 start-page: 2191 year: 2020 ident: 18919_CR43 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2020.03.271 – volume: 8 start-page: 167059 year: 2020 ident: 18919_CR37 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3022855 – volume: 244 start-page: 108505 year: 2022 ident: 18919_CR47 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2022.108505 – volume: 49 start-page: 3235 year: 2019 ident: 18919_CR6 publication-title: Appl Intell doi: 10.1007/s10489-019-01436-1 – ident: 18919_CR3 doi: 10.1007/s11042-023-16436-0 – volume: 81 start-page: 8499 issue: 6 year: 2022 ident: 18919_CR1 publication-title: Multimed Tools Applic doi: 10.1007/s11042-021-11747-6 – volume: 102 start-page: 108214 year: 2022 ident: 18919_CR53 publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2022.108214 – volume: 13 start-page: 557 issue: 4 year: 2021 ident: 18919_CR52 publication-title: Symmetry doi: 10.3390/sym13040557 – volume: 8 start-page: 74571 year: 2020 ident: 18919_CR38 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2988854 – ident: 18919_CR54 doi: 10.1155/2023/7107663 – volume: 14 start-page: 47 issue: 1 year: 2021 ident: 18919_CR16 publication-title: Evol Intel doi: 10.1007/s12065-019-00291-w – volume: 10 start-page: 1077 issue: 9 year: 2021 ident: 18919_CR11 publication-title: Electronics doi: 10.3390/electronics10091077 – volume: 58 start-page: 102804 year: 2021 ident: 18919_CR22 publication-title: J Inf Secur Applic – volume: 8 start-page: 73907 year: 2020 ident: 18919_CR39 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2988055 – volume: 25 start-page: 121 year: 2021 ident: 18919_CR20 publication-title: Pers Ubiquit Comput doi: 10.1007/s00779-019-01332-y – volume: 10 start-page: 2269 year: 2021 ident: 18919_CR36 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3137201 – volume: 12 start-page: 1695 issue: 10 year: 2020 ident: 18919_CR21 publication-title: Symmetry doi: 10.3390/sym12101695 – volume: 125 start-page: 231 issue: 1 year: 2022 ident: 18919_CR32 publication-title: Wirel Pers Commun doi: 10.1007/s11277-022-09548-7 – volume: 81 start-page: 26739 year: 2021 ident: 18919_CR12 publication-title: Multimed Tools Applic doi: 10.1007/s11042-021-10640-6 – volume: 113 start-page: 255 year: 2020 ident: 18919_CR51 publication-title: Futur Gener Comput Syst doi: 10.1016/j.future.2020.07.020 – volume: 81 start-page: 4185 issue: 3 year: 2022 ident: 18919_CR15 publication-title: Multimed Tools Applic doi: 10.1007/s11042-021-11740-z – volume: 31 start-page: e3868 issue: 3 year: 2020 ident: 18919_CR14 publication-title: Trans Emerg Telecommun Technol doi: 10.1002/ett.3868 – volume: 60 start-page: 102866 year: 2021 ident: 18919_CR34 publication-title: J Inf Secur Applic – volume: 225 start-page: 109662 year: 2023 ident: 18919_CR26 publication-title: Comput Netw doi: 10.1016/j.comnet.2023.109662 – ident: 18919_CR28 doi: 10.1007/s11042-023-17677-9 – volume: 21 start-page: 7016 issue: 21 year: 2021 ident: 18919_CR18 publication-title: Sensors doi: 10.3390/s21217016 – volume: 33 start-page: e3803 issue: 3 year: 2022 ident: 18919_CR31 publication-title: Trans Emerg Telecommun Technol doi: 10.1002/ett.3803 – ident: 18919_CR50 doi: 10.1155/2023/1982173 – volume: 8 start-page: 132911 year: 2020 ident: 18919_CR40 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3009843 – volume: 10 start-page: 99837 year: 2022 ident: 18919_CR46 publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3206425 – volume: 8 start-page: 22 issue: 1 year: 2020 ident: 18919_CR45 publication-title: Syst Sci Control Eng doi: 10.1080/21642583.2019.1708830 – volume: 81 start-page: 1997 issue: 2 year: 2022 ident: 18919_CR10 publication-title: Multimed Tools Applic doi: 10.1007/s11042-021-11423-9 – volume: 73 start-page: 6579 issue: 3 year: 2022 ident: 18919_CR25 publication-title: CMC-Comput Mater Continua doi: 10.32604/cmc.2022.031303 – volume: 8 start-page: 74571 year: 2020 ident: 18919_CR2 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2988854 – ident: 18919_CR4 doi: 10.1109/ACCESS.2019.2895334 – volume: 15 start-page: 423 issue: 1 year: 2023 ident: 18919_CR23 publication-title: Int J Inf Technol – volume: 252 start-page: 109364 year: 2022 ident: 18919_CR30 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2022.109364 – volume: 81 start-page: 41225 issue: 28 year: 2022 ident: 18919_CR7 publication-title: Multimed Tools Applic doi: 10.1007/s11042-022-12330-3 – volume: 9 start-page: 1771 issue: 11 year: 2020 ident: 18919_CR19 publication-title: Electronics doi: 10.3390/electronics9111771 – ident: 18919_CR24 doi: 10.3390/sym14010161 – volume: 92 start-page: 101752 year: 2020 ident: 18919_CR8 publication-title: Comput Secur doi: 10.1016/j.cose.2020.101752 – volume: 12 start-page: 9555 issue: 10 year: 2021 ident: 18919_CR35 publication-title: J Ambient Intell Humaniz Comput doi: 10.1007/s12652-020-02696-3 – volume: 11 start-page: 371 issue: 04 year: 2023 ident: 18919_CR48 publication-title: J Data Anal Inf Process – volume: 20 start-page: 100368 year: 2020 ident: 18919_CR9 publication-title: Inform Med Unlocked doi: 10.1016/j.imu.2020.100368 – volume: 22 start-page: 8417 issue: 21 year: 2022 ident: 18919_CR29 publication-title: Sensors doi: 10.3390/s22218417
SSID	ssj0016524
Score	2.3757925
Snippet	Our world is rapidly evolving toward the Internet of Things (IoT), that connects all gadgets to digital services and simplifies our lives. As IoT devices...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	4907
SubjectTerms	Accuracy Algorithms Computer Communication Networks Computer Science Computing time Data Structures and Information Theory Edge computing Error detection Internet of Things Intrusion detection systems Machine learning Multimedia Information Systems Network latency Optimization models Special Purpose and Application-Based Systems Workload
Title	Optimized deep autoencoder and BiLSTM for intrusion detection in IoTs-Fog computing
URI	https://link.springer.com/article/10.1007/s11042-024-18919-0 https://www.proquest.com/docview/3180016018
Volume	84
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-7721 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016524 issn: 1573-7721 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDLZgcIADgwFiMFAO3CBSm76PgJhAgoHYmHarmsdQJWgnOjjw63GydgUESHDpoU3Syo7tr3I-G-BQk3CYN_YoXhzqjqOQcikCGskkUp6weGLavQ2vgl4vHI2i25IUVlSn3auUpPHUNdnN1lQSjCnUDiNNvVmEJQx3oW7YcNcfznMHvsfckh7z_bzPIajGlV9SoSbCdJv_-7Z1WCsRJTmZbYENWFBZC5pVtwZSGm8LVj-UHtyE_g36iqf0TUkilZqQ5GWa65qWEqckmSSn6VV_cE0Q0pI008QM1B-OnJqTWxneI5f5oKDd_IEI8ypcdQvuu-eDswtaNliggjmuRaUvlWbOJswfc24JtEDJ_DDypZMItE4EF0z4tuB8rHGfJQKPWx666sROEPYpZxsaWZ6pHSCRx4JQuQwRiety_K1GRxE5wuYaozFLtsGuZB6Lsvq4boLxGNd1k7UMY5RhbGQYW204ms-ZzGpv_Dq6U6kyLu2wiNFjmU7adtiG40p19eOfV9v92_A9WGG6MbA5nNaBBupF7cOyeJ2mxfOB2Z_vTfPdtw
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8JAEJ4omqgHH6gRRd2DN92k3T5oj2okEAsaQcKt6T5qSLQQQQ_-emeXFtSoiV56aHe3zczOzNfMfjMAJ5qEw7zUo3hxqJuGAeVS1Ggok1B5wuKJaffWi2rtdtDvh7c5KWxcnHYvUpLGU8_JbramkmBMoXYQaurNIiy5GLF0xfy7Tm-WO_A95ub0mO_nfQ5Bc1z5JRVqIkx943_ftgnrOaIk59MtsAULKivDRtGtgeTGW4a1D6UHt6Fzg77iafCmJJFKjUjyMhnqmpYSpySZJBeDqNNtEYS0ZJBpYgbqD0dOzMmtDO-R5rA7pvXhAxHmVbjqDtzXr7qXDZo3WKCCOa5FpS-VZs4mzE85twRaoGR-EPrSSQRaJ4ILJnxbcJ5q3GeJmsctD111YicI-5SzC6VsmKk9IKHHaoFyGSIS1-X4W42OInSEzTVGY5asgF3IPBZ59XHdBOMxntdN1jKMUYaxkWFsVeB0Nmc0rb3x6-hqoco4t8NxjB7LdNK2gwqcFaqbP_55tf2_DT-GlUa3FcVRs319AKtMNwk2B9WqUEIdqUNYFq-Twfj5yOzVd7s24Js
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT8JAEJ4oGqMHUdSIou7Bm25otw_aoy8iEZEEJNya7qOGRAsR9OCvd3ZpBY2aGC89tLvbzczO7LfZ-WYAjjUJh3mJR_HhUDcJA8qlqNFQxqHyhMVjU-6t16y1WkG_H7bnWPwm2j2_kpxyGnSWpnRSHcmkOiO-2ZpWgvsLtYNQ03AWYcnVgfT6vN7pfdwj-B5zM6rM9_0-b0czjPnlWtTsNvXi_-e5AesZ0iRn06WxCQsqLUExr-JAMqMuwdpcSsIt6NyhD3kavClJpFIjEr9MhjrXpcQucSrJ-aDZ6d4ShLpkkGrCBuoVW05MRFeK70hj2B3T-vCBCPMrHHUb7utX3YtrmhVeoII5rkWlL5Vm1MbMTzi3BFqmZH4Q-tKJBVotgg4mfFtwnmg8aImaxy0PXXhsxwgHlbMDhXSYql0gocdqgXIZIhXX5XjcRgcSOsLmGrsxS5bBzuUfiSwruS6O8RjN8ilrGUYow8jIMLLKcPLRZzTNyfFr60qu1iizz3GEnsxU2LaDMpzmapx9_nm0vb81P4KV9mU9ajZaN_uwynTtYBO_VoECqkgdwLJ4nQzGz4dm2b4DdEbpfw
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=Optimized+deep+autoencoder+and+BiLSTM+for+intrusion+detection+in+IoTs-Fog+computing&rft.jtitle=Multimedia+tools+and+applications&rft.date=2025-03-01&rft.pub=Springer+Nature+B.V&rft.issn=1380-7501&rft.eissn=1573-7721&rft.volume=84&rft.issue=8&rft.spage=4907&rft.epage=4943&rft_id=info:doi/10.1007%2Fs11042-024-18919-0&rft.externalDBID=HAS_PDF_LINK
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1573-7721&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1573-7721&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1573-7721&client=summon