A Modified Multi-objective Particle Swarm Optimizer-Based Lévy Flight: An Approach Toward Intrusion Detection in Internet of Things

The emerging of the Internet of things (IoT), and more, the advent of the Internet of everything have revolutionized the computer networks industry. The high diversity of IoT devices, its protocols and standards, and its limited computational resources have led to the appearance of novel security ch...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Arabian journal for science and engineering (2011) Ročník 45; číslo 8; s. 6081 - 6108
Hlavní autori:	Habib, Maria, Aljarah, Ibrahim, Faris, Hossam
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020 Springer Nature B.V
Predmet:	Authentication Computer networks Encryption Engineering Evolutionary algorithms Humanities and Social Sciences Internet of Things Intrusion detection systems multidisciplinary Multiple objective analysis Network security Particle swarm optimization Protocol (computers) Research Article-Computer Engineering and Computer Science Science Multi-objective particle swarm optimization Internet of Things Botnets Classification Lévy flight Multi-objective feature selection
ISSN:	2193-567X, 1319-8025, 2191-4281
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	The emerging of the Internet of things (IoT), and more, the advent of the Internet of everything have revolutionized the computer networks industry. The high diversity of IoT devices, its protocols and standards, and its limited computational resources have led to the appearance of novel security challenges. Hence, the traditional security countermeasures of encryption and authentication are insufficient. Promoting the network security is a fundamental concern for practitioners for safeguarding their economical and industrial strategies. Intrusion detection systems (IDSs) are the major solutions for protecting Internet-connected frameworks at the network-level. But, more importantly, is how to convert the traditional IDSs into intelligent IDSs that resemble the intelligent IoT. This paper presents a new approach for converting the traditional IDSs into smart, evolutionary, and multi-objective IDSs for IoT networks. Moreover, this article presents a modified algorithm for IDSs that tackles the problem of feature selection. The modified algorithm stands on the integration of multi-objective particle swarm optimization with Lévy flight randomization component (MOPSO-Lévy); the modified MOPSO-Lévy has been tested on real IoT network data that is drawn from UCI repository. MOPSO-Lévy has achieved superior performance results when compared with state-of-the-art evolutionary multi-objective algorithms.
AbstractList	The emerging of the Internet of things (IoT), and more, the advent of the Internet of everything have revolutionized the computer networks industry. The high diversity of IoT devices, its protocols and standards, and its limited computational resources have led to the appearance of novel security challenges. Hence, the traditional security countermeasures of encryption and authentication are insufficient. Promoting the network security is a fundamental concern for practitioners for safeguarding their economical and industrial strategies. Intrusion detection systems (IDSs) are the major solutions for protecting Internet-connected frameworks at the network-level. But, more importantly, is how to convert the traditional IDSs into intelligent IDSs that resemble the intelligent IoT. This paper presents a new approach for converting the traditional IDSs into smart, evolutionary, and multi-objective IDSs for IoT networks. Moreover, this article presents a modified algorithm for IDSs that tackles the problem of feature selection. The modified algorithm stands on the integration of multi-objective particle swarm optimization with Lévy flight randomization component (MOPSO-Lévy); the modified MOPSO-Lévy has been tested on real IoT network data that is drawn from UCI repository. MOPSO-Lévy has achieved superior performance results when compared with state-of-the-art evolutionary multi-objective algorithms.
Author	Aljarah, Ibrahim Habib, Maria Faris, Hossam
Author_xml	– sequence: 1 givenname: Maria surname: Habib fullname: Habib, Maria organization: King Abdullah II School for Information Technology, The University of Jordan – sequence: 2 givenname: Ibrahim surname: Aljarah fullname: Aljarah, Ibrahim email: i.aljarah@ju.edu.jo organization: King Abdullah II School for Information Technology, The University of Jordan – sequence: 3 givenname: Hossam surname: Faris fullname: Faris, Hossam organization: King Abdullah II School for Information Technology, The University of Jordan
BookMark	eNp9kMtKAzEUhoMoeH0BVwHX0WQyk5m4q3ehRcEK7kImk2kj06QmaUXXvozP4YuZaQXBhXDgHA7_dy7_Lti0zmoADgk-JhiXJ4FQyjjCGUY4z0uG-AbYyQgnKM8qsrmqKSpY-bQNDkIwNc4rygtC6A74GMCRa0xrdANHiy4a5OpnraJZangvfTSq0_DhVfoZvJtHMzPv2qMzGZJ8-PW5fINXnZlM4ykcWDiYz72TagrHLgENvLXRL4JxFl7o2M9MlbF9W3urI3QtHE-NnYR9sNXKLuiDn7wHHq8ux-c3aHh3fXs-GCJFCY-IUZZJWVOsFFZFUxctrosUtCh0TTmpiKalbmrV8qbFFa-Svi0ryRnDDBNF98DRem6682WhQxTPbuFtWimyPCtYRnnFkypbq5R3IXjdirk3M-nfBMGiN1ysDRfJcLEyXPRQ9QdSJsr-5eil6f5H6RoNaY-daP971T_UNytTmKc
CitedBy_id	crossref_primary_10_1007_s00354_022_00188_w crossref_primary_10_3390_math10030464 crossref_primary_10_1057_s41288_022_00266_6 crossref_primary_10_3390_s21227611 crossref_primary_10_3390_math10152785 crossref_primary_10_1177_09266801241289769 crossref_primary_10_1007_s11042_022_13836_6 crossref_primary_10_1155_2022_2882643 crossref_primary_10_3390_s22218566 crossref_primary_10_1007_s10791_025_09708_w crossref_primary_10_1016_j_iot_2023_100952 crossref_primary_10_3389_fcomp_2023_997159 crossref_primary_10_1016_j_knosys_2022_108505 crossref_primary_10_3390_app12178676 crossref_primary_10_1007_s11042_024_19962_7 crossref_primary_10_1093_comjnl_bxae008 crossref_primary_10_1016_j_future_2020_05_020 crossref_primary_10_1155_2021_7504806 crossref_primary_10_1002_nem_2296 crossref_primary_10_1109_ACCESS_2021_3071450 crossref_primary_10_1016_j_measen_2023_100700 crossref_primary_10_1016_j_knosys_2024_112950 crossref_primary_10_1007_s13369_020_05301_z crossref_primary_10_1016_j_swevo_2025_101984 crossref_primary_10_1109_ACCESS_2025_3531216
Cites_doi	10.1016/j.future.2017.08.043 10.1016/j.ijinfomgt.2019.04.006 10.1109/JIOT.2018.2883344 10.1007/s12652-019-01387-y 10.1016/j.future.2017.07.060 10.1007/978-3-030-12127-3_3 10.1109/TEVC.2003.816583 10.1109/TEVC.2004.826067 10.1109/CC.2018.8331989 10.1145/1656274.1656278 10.1109/TENCON.2019.8929510 10.5220/0006048201710177 10.1016/j.measurement.2017.12.014 10.1080/00031305.1992.10475879 10.1007/978-3-642-20662-7_2 10.1007/978-3-319-92639-1_30 10.1007/978-3-030-12127-3_8 10.1109/ACCESS.2018.2800664 10.1109/MPRV.2018.03367731 10.1007/978-3-319-70688-7_2 10.1051/matecconf/201815901053 10.1109/TEVC.2007.892759 10.1016/j.camwa.2008.09.023 10.1093/biomet/52.1-2.203 10.1109/MSP.2018.2825478 10.1007/s10586-019-02913-5 10.1007/978-3-319-92058-0_56 10.1155/2018/2492956 10.1016/j.jnca.2017.02.009 10.1109/AEECT.2015.7360576 10.1109/COMST.2020.2986444 10.1016/j.swevo.2012.09.002 10.1007/s12652-017-0558-5 10.1201/9780429504044-8 10.1007/s10586-018-1847-2 10.1109/MASCOTS.2018.00038 10.1007/s00500-008-0323-y 10.1109/CEC.2008.4631192 10.1007/s10489-017-0967-3 10.1109/JIOT.2018.2871719 10.1016/j.comnet.2019.01.023 10.1142/S0218213016500330 10.1007/s00521-019-04015-0 10.1016/j.asoc.2018.05.049 10.1007/s00521-018-3801-x 10.3390/s18093030 10.1007/978-1-4614-6849-3 10.1007/3-540-45356-3_83
ContentType	Journal Article
Copyright	King Fahd University of Petroleum & Minerals 2020 King Fahd University of Petroleum & Minerals 2020.
Copyright_xml	– notice: King Fahd University of Petroleum & Minerals 2020 – notice: King Fahd University of Petroleum & Minerals 2020.
DBID	AAYXX CITATION
DOI	10.1007/s13369-020-04476-9
DatabaseName	CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2191-4281
EndPage	6108
ExternalDocumentID	10_1007_s13369_020_04476_9
GroupedDBID	-EM 0R~ 203 2KG 406 AAAVM AACDK AAHNG AAIAL AAJBT AANZL AARHV AASML AATNV AATVU AAUYE AAYTO AAYZH ABAKF ABDBF ABDZT ABECU ABFTD ABFTV ABJNI ABJOX ABKCH ABMQK ABQBU ABSXP ABTEG ABTKH ABTMW ABXPI ACAOD ACBXY ACDTI ACHSB ACMDZ ACMLO ACOKC ACPIV ACUHS ACZOJ ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEJRE AEMSY AEOHA AESKC AEVLU AEXYK AFBBN AFLOW AFQWF AGAYW AGJBK AGMZJ AGQEE AGQMX AGRTI AHAVH AHBYD AHSBF AIAKS AIGIU AILAN AITGF AJBLW AJRNO AJZVZ ALFXC ALMA_UNASSIGNED_HOLDINGS AMXSW AMYLF AOCGG AXYYD BGNMA CSCUP DDRTE DNIVK DPUIP EBLON EBS EIOEI EJD ESX FERAY FIGPU FINBP FNLPD FSGXE GGCAI GQ6 GQ7 H13 HG6 I-F IKXTQ IWAJR J-C JBSCW JZLTJ L8X LLZTM M4Y MK~ NPVJJ NQJWS NU0 O9J PT4 ROL RSV SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE TSG TUS UOJIU UTJUX UZXMN VFIZW Z5O Z7R Z7V Z7X Z7Y Z7Z Z81 Z83 Z85 Z88 ZMTXR ~8M AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION 06D 0VY 23M 29~ 2KM 30V 408 5GY 96X AAJKR AARTL AAYIU AAYQN AAZMS ABTHY ACGFS ACKNC ADHHG ADHIR AEGNC AEJHL AENEX AEPYU AETCA AFWTZ AFZKB AGDGC AGWZB AGYKE AHYZX AIIXL AMKLP AMYQR ANMIH AYJHY ESBYG FFXSO FRRFC FYJPI GGRSB GJIRD GX1 HMJXF HRMNR HZ~ I0C IXD J9A KOV O93 OVT P9P R9I RLLFE S27 S3B SEG SHX T13 U2A UG4 VC2 W48 WK8 ~A9
ID	FETCH-LOGICAL-c319t-6362aab30cc0c5db5f0b50b5355eb39181e37edbcf9df0898362f78a9660601c3
IEDL.DBID	RSV
ISICitedReferencesCount	29
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000522934600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2193-567X 1319-8025
IngestDate	Mon Jun 30 09:04:16 EDT 2025 Tue Nov 18 22:23:21 EST 2025 Sat Nov 29 02:51:32 EST 2025 Fri Feb 21 02:36:49 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	8
Keywords	Multi-objective particle swarm optimization Internet of Things Botnets Classification Lévy flight Multi-objective feature selection
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-6362aab30cc0c5db5f0b50b5355eb39181e37edbcf9df0898362f78a9660601c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
PQID	2425623989
PQPubID	2044268
PageCount	28
ParticipantIDs	proquest_journals_2425623989 crossref_primary_10_1007_s13369_020_04476_9 crossref_citationtrail_10_1007_s13369_020_04476_9 springer_journals_10_1007_s13369_020_04476_9
PublicationCentury	2000
PublicationDate	2020-08-01
PublicationDateYYYYMMDD	2020-08-01
PublicationDate_xml	– month: 08 year: 2020 text: 2020-08-01 day: 01
PublicationDecade	2020
PublicationPlace	Berlin/Heidelberg
PublicationPlace_xml	– name: Berlin/Heidelberg – name: Heidelberg
PublicationTitle	Arabian journal for science and engineering (2011)
PublicationTitleAbbrev	Arab J Sci Eng
PublicationYear	2020
Publisher	Springer Berlin Heidelberg Springer Nature B.V
Publisher_xml	– name: Springer Berlin Heidelberg – name: Springer Nature B.V
References	AzmoodehADehghantanhaAContiMChooK-KRDetecting crypto-ransomware in iot networks based on energy consumption footprintJ. Ambient Intell. Humaniz. Comput.20189411411152 Mohemmed, A.W.; Zhang, M.: Evaluation of particle swarm optimization based centroid classifier with different distance metrics. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 2929–2932. IEEE (2008) FarisHAljarahIMirjaliliSImproved monarch butterfly optimization for unconstrained global search and neural network trainingAppl. Intell.201848445464 ZhanyangXRenhaoGHuangTXiangHZhangXQiLXiaolongXAn IoT-oriented offloading method with privacy preservation for cloudlet-enabled wireless metropolitan area networksSensors20181893030 SelvaraniPSureshAMalarvizhiNSecure and optimal authentication framework for cloud management using HGAPSO algorithmCluster Comput.201822240074016 AltmanNSAn introduction to kernel and nearest-neighbor nonparametric regressionAm. Stat.19924631751851183070 ChechkinAVMetzlerRKlafterJGoncharVYIntroduction to the theory of Lévy flightsAnomalous Transp. Found. Appl.2008492431451 LiDDengLLeeMWangHIoT data feature extraction and intrusion detection system for smart cities based on deep migration learningInt. J. Inf. Manag.201949533545 da CostaKAPPapaJPLisboaCOMunozRde AlbuquerqueVHCInternet of things: a survey on machine learning-based intrusion detection approachesComput. Netw.2019151147157 AlhajriRZagroubaRAl-HaidariFSurvey for anomaly detection of iot botnets using machine learning auto-encodersInt. J. Appl. Eng. Res.2019141024172421 Corne, D.W; Jerram, N.R.; Knowles, J.D.; Oates, M.J.: Pesa-ii: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 283–290. Morgan Kaufmann Publishers Inc. (2001) Coello, C.A.C.; Lechuga, M.S.: Mopso: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No. 02TH8600), vol. 2, pp. 1051–1056. IEEE (2002) ZarpelaoBBMianiRSKawakaniCTde AlvarengaSCA survey of intrusion detection in internet of thingsJ. Netw. Comput. Appl.2017842537 DebKKalyanmoyDMulti-objective Optimization Using Evolutionary Algorithms2001New YorkWiley0970.90091 MirjaliliSLewisAS-shaped versus v-shaped transfer functions for binary particle swarm optimizationSwarm Evolut. Comput.20139114 KuhnMJohnsonKApplied Predictive Modeling2013BerlinSpringer1306.62014 Mohamed, T.A.; Otsuka, T.; Ito, T.: Towards machine learning based IoT intrusion detection service. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 580–585. Springer (2018) Yang, X.-S.; Metaheuristic optimization: algorithm analysis and open problems. In: International Symposium on Experimental Algorithms, pp. 21–32. Springer, Berlin (2011) Heidari, A.A.; Faris, H.; Mirjalili, S.; Aljarah, I.; Mafarja, M.: Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks. In: Nature-Inspired Optimizers, pp. 23–46. Springer, Cham (2020) KesavamoorthyRSoundarKRSwarm intelligence based autonomous DDOS attack detection and defense using multi agent systemCluster Comput.201822494699476 HallMFrankEHolmesGPfahringerBReutemannPWittenIHThe weka data mining software: an updateACM SIGKDD Explor. Newslett.20091111018 Almomani, A.; Alweshah, M.; Al, S.: Metaheuristic algorithms-based feature selection approach for intrusion detection. In: Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices (2019) Hussain, F.; Hussain, R.; Hassan, S.A.; Hossain, E.: Machine learning in IoT security: current solutions and future challenges. arXiv preprint arXiv:1904.05735 (2019) GehanEAA generalized Wilcoxon test for comparing arbitrarily singly-censored samplesBiometrika1965521–22032242071300133.41901 SangeethaALBharathiNGaneshABRadhakrishnanTKParticle swarm optimization tuned cascade control system in an internet of things (IoT) environmentMeasurement20181178089 RanaSHossainSShounHIAbul KashemMAn effective lightweight cryptographic algorithm to secure resource-constrained devicesInt. J. Adv. Comput. Sci. Appl.2018911267275 Deng, L.; Li, D.; Yao, X.; Cox, D.; Wang, H.: Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Comput. 1–16 (2018) Mishra, S.; Sagban, R.; Yakoob, A.; Gandhi, N.: Swarm intelligence in anomaly detection systems: an overview. Int. J. Comput. Appl. 1–10 (2018) YangX-SNature-Inspired Metaheuristic Algorithms2010BeckingtonLuniver Press Sreenivasamurthy, S.; Obraczka, K.: Clustering for load balancing and energy efficiency in IoT applications. In: 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 319–332. IEEE (2018) Faris, H.; Aljarah, I.; Alqatawna, J.F.: Optimizing feedforward neural networks using krill herd algorithm for e-mail spam detection. In: 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–5. IEEE (2015) Faris, H.; Aljarah, I.; Mirjalili, S.; Castillo, P.A.; Guervós, J.J.M.: EvoloPy: an open-source nature-inspired optimization framework in Python. In: IJCCI (ECTA), pp. 171–177 (2016) LiSBiFChenWMiaoXLiuJTangCAn improved information security risk assessments method for cyber-physical-social computing and networkingIEEE Access201861031110319 LeeC-YYaoXEvolutionary programming using mutations based on the Lévy probability distributionIEEE Trans. Evol. Comput.200481113 Suman, C.; Tripathy, S.; Saha, S.: Building an effective intrusion detection system using unsupervised feature selection in multi-objective optimization framework. arXiv preprint arXiv:1905.06562 (2019) LiJZhaoZLiRZhangHZhangTAi-based two-stage intrusion detection for software defined iot networksIEEE Internet Things J.20186220932102 CoelloCACPulidoGTHandling multiple objectives with particle swarm optimizationIEEE Trans. Evol. Comput.200483256279 Xue, Y.; Jia, W.; Zhao, X.; Pang, W.: An evolutionary computation based feature selection method for intrusion detection. Secur. Commun. Netw. (2018) Sanchez-Pi, N.; Martí, L.; Molina, J.M.: Applying voreal for IoT intrusion detection. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 363–374. Springer (2018) XiaoLWanXXiaozhenLZhangYDiWIot security techniques based on machine learning: how do iot devices use ai to enhance security?IEEE Signal Process. Mag.20183554149 Dheeru, D.; Taniskidou, E.K.: UCI machine learning repository (2017) Al Shorman, A.; Faris, H.; Aljarah, I.: Unsupervised intelligent system based on one class support vector machine and grey wolf optimization for iot botnet detection. J. Ambient Intell. Humaniz. Comput. 1–17 (2019) Aljarah, I.; Mafarja, M.; Heidari, A.A.; Faris, H.; Mirjalili, S.: Multi-verse optimizer: theory, literature review, and application in data clustering. In: Nature-Inspired Optimizers, pp. 123–141. Springer, Cham (2020) FarisHAljarahIAl-MadiNMirjaliliSOptimizing the learning process of feedforward neural networks using lightning search algorithmInt. J. Artif. Intell. Tools20162561650033 AljarahIFarisHMirjaliliSAl-MadiNShetaAMafarjaMEvolving neural networks using bird swarm algorithm for data classification and regression applicationsCluster Comput.201922413171345 Zitzler, E.; Laumanns, M.; Thiele, L.: Spea2: improving the strength Pareto evolutionary algorithm. TIK-report, 103 (2001) Alcalá-FdezJSanchezLGarciaSdel JesusMJVenturaSGarrellJMOteroJRomeroCBacarditJRivasVMKeel: a software tool to assess evolutionary algorithms for data mining problemsSoft Comput.2009133307318 Elhoseny, M.; Shankar, K.; Lakshmanaprabu, S.K.; Maseleno, A.; Arunkumar, N.: Hybrid optimization with cryptography encryption for medical image security in internet of things. Neural Comput. Appl. 1–15 (2018) Deb, K.; Agrawal, S.; Pratap, A.; Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In: International Conference on Parallel Problem Solving from Nature, pp. 849–858. Springer, Berlin (2000) ZhangQLiHMOEA/D: a multiobjective evolutionary algorithm based on decompositionIEEE Trans. Evol. Comput.2007116712731 Conti, M.; Dehghantanha, A.; Franke, K., Watson, S.: Internet of things security and forensics: challenges and opportunities (2018) DiroAAChilamkurtiNDistributed attack detection scheme using deep learning approach for internet of thingsFuture Gener. Comput. Syst.201882761768 RathoreSParkJHSemi-supervised learning based distributed attack detection framework for IoTAppl. Soft Comput.2018727989 HanJPeiJKamberMData Mining: Concepts and Techniques2011AmsterdamElsevier1230.68018 MeidanYBohadanaMMathovYMirskyYShabtaiABreitenbacherDEloviciYN-baiot—network-based detection of iot botnet attacks using deep autoencodersIEEE Pervasive Comput.20181731222 Kennedy, J.; Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108. IEEE (1997) Hemdan, E.E.-D.; Manjaiah, D.H.: Cybercrimes investigation and intrusion detection in internet of things based on data science methods. In: Cognitive Computing for Big Data Systems Over IoT, pp. 39–62. Springer, Berlin (2018) Tama, B.A.; Rhee, K.-H.: An integration of PSO-based feature selection and random forest for anomaly detection in ToT network. In: MATEC Web of Conferences, vol. 159, pp. 01053. EDP Sciences (2018) ZhengXLiuHA hybrid vertical mutation and self-adaptation based MOPSOComput. Math. Appl.20095711–122030203825233691186.90137 Heidari, A.A.; Aljarah, I.; Faris, H.; Chen, H.; Luo, J.; Mirjalili, S.: An enhanced associative learning-based exploratory whale optimizer for global optimization. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04015-0 MoustafaNTurnbullBChooK-KRAn NS Altman (4476_CR54) 1992; 46 S Rathore (4476_CR31) 2018; 72 J Han (4476_CR53) 2011 C-Y Lee (4476_CR60) 2004; 8 4476_CR47 4476_CR48 R Alhajri (4476_CR29) 2019; 14 BB Zarpelao (4476_CR19) 2017; 84 4476_CR49 KAP da Costa (4476_CR26) 2019; 151 4476_CR40 4476_CR41 H Faris (4476_CR5) 2016; 25 H Faris (4476_CR4) 2018; 48 Y Meidan (4476_CR28) 2018; 17 J Alcalá-Fdez (4476_CR61) 2009; 13 4476_CR14 4476_CR15 4476_CR12 4476_CR56 X Zhanyang (4476_CR37) 2018; 18 4476_CR16 4476_CR17 4476_CR51 4476_CR10 4476_CR55 4476_CR1 4476_CR2 X-S Yang (4476_CR3) 2010 4476_CR6 4476_CR7 4476_CR25 4476_CR8 4476_CR9 L Xiao (4476_CR22) 2018; 35 S Li (4476_CR46) 2018; 6 4476_CR21 AV Chechkin (4476_CR58) 2008; 49 CAC Coello (4476_CR50) 2004; 8 K Deb (4476_CR13) 2001 D Li (4476_CR24) 2019; 49 AA Diro (4476_CR23) 2018; 82 S Mirjalili (4476_CR52) 2013; 9 J Li (4476_CR45) 2018; 6 I Aljarah (4476_CR11) 2019; 22 AL Sangeetha (4476_CR39) 2018; 117 4476_CR34 4476_CR35 M Hall (4476_CR63) 2009; 11 4476_CR38 P Selvarani (4476_CR43) 2018; 22 A Azmoodeh (4476_CR27) 2018; 9 4476_CR32 R Kesavamoorthy (4476_CR42) 2018; 22 4476_CR33 Q Zhang (4476_CR18) 2007; 11 4476_CR30 M Kuhn (4476_CR59) 2013 X Zheng (4476_CR57) 2009; 57 S Rana (4476_CR36) 2018; 9 EA Gehan (4476_CR62) 1965; 52 Z Sun (4476_CR44) 2018; 15 N Moustafa (4476_CR20) 2018; 6
References_xml	– reference: Mishra, S.; Sagban, R.; Yakoob, A.; Gandhi, N.: Swarm intelligence in anomaly detection systems: an overview. Int. J. Comput. Appl. 1–10 (2018) – reference: Hussain, F.; Hussain, R.; Hassan, S.A.; Hossain, E.: Machine learning in IoT security: current solutions and future challenges. arXiv preprint arXiv:1904.05735 (2019) – reference: MeidanYBohadanaMMathovYMirskyYShabtaiABreitenbacherDEloviciYN-baiot—network-based detection of iot botnet attacks using deep autoencodersIEEE Pervasive Comput.20181731222 – reference: Coello, C.A.C.; Lechuga, M.S.: Mopso: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No. 02TH8600), vol. 2, pp. 1051–1056. IEEE (2002) – reference: LiJZhaoZLiRZhangHZhangTAi-based two-stage intrusion detection for software defined iot networksIEEE Internet Things J.20186220932102 – reference: RathoreSParkJHSemi-supervised learning based distributed attack detection framework for IoTAppl. Soft Comput.2018727989 – reference: ZhengXLiuHA hybrid vertical mutation and self-adaptation based MOPSOComput. Math. Appl.20095711–122030203825233691186.90137 – reference: Faris, H.; Aljarah, I.; Alqatawna, J.F.: Optimizing feedforward neural networks using krill herd algorithm for e-mail spam detection. In: 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–5. IEEE (2015) – reference: Deng, L.; Li, D.; Yao, X.; Cox, D.; Wang, H.: Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Comput. 1–16 (2018) – reference: Kennedy, J.; Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108. IEEE (1997) – reference: MirjaliliSLewisAS-shaped versus v-shaped transfer functions for binary particle swarm optimizationSwarm Evolut. Comput.20139114 – reference: AltmanNSAn introduction to kernel and nearest-neighbor nonparametric regressionAm. Stat.19924631751851183070 – reference: ZhangQLiHMOEA/D: a multiobjective evolutionary algorithm based on decompositionIEEE Trans. Evol. Comput.2007116712731 – reference: KuhnMJohnsonKApplied Predictive Modeling2013BerlinSpringer1306.62014 – reference: YangX-SNature-Inspired Metaheuristic Algorithms2010BeckingtonLuniver Press – reference: Mohamed, T.A.; Otsuka, T.; Ito, T.: Towards machine learning based IoT intrusion detection service. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 580–585. Springer (2018) – reference: Xue, Y.; Jia, W.; Zhao, X.; Pang, W.: An evolutionary computation based feature selection method for intrusion detection. Secur. Commun. Netw. (2018) – reference: Faris, H.; Aljarah, I.; Mirjalili, S.; Castillo, P.A.; Guervós, J.J.M.: EvoloPy: an open-source nature-inspired optimization framework in Python. In: IJCCI (ECTA), pp. 171–177 (2016) – reference: FarisHAljarahIMirjaliliSImproved monarch butterfly optimization for unconstrained global search and neural network trainingAppl. Intell.201848445464 – reference: Zitzler, E.; Laumanns, M.; Thiele, L.: Spea2: improving the strength Pareto evolutionary algorithm. TIK-report, 103 (2001) – reference: Sreenivasamurthy, S.; Obraczka, K.: Clustering for load balancing and energy efficiency in IoT applications. In: 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 319–332. IEEE (2018) – reference: XiaoLWanXXiaozhenLZhangYDiWIot security techniques based on machine learning: how do iot devices use ai to enhance security?IEEE Signal Process. Mag.20183554149 – reference: Téllez, Nadim; Jimeno, Miguel; Salazar, Augusto; Nino-Ruiz, E: A tabu search method for load balancing in fog computing. Int. J. Artif. Intell. 16(2) (2018) – reference: Hemdan, E.E.-D.; Manjaiah, D.H.: Cybercrimes investigation and intrusion detection in internet of things based on data science methods. In: Cognitive Computing for Big Data Systems Over IoT, pp. 39–62. Springer, Berlin (2018) – reference: Aljarah, I.; Mafarja, M.; Heidari, A.A.; Faris, H.; Mirjalili, S.: Multi-verse optimizer: theory, literature review, and application in data clustering. In: Nature-Inspired Optimizers, pp. 123–141. Springer, Cham (2020) – reference: ZarpelaoBBMianiRSKawakaniCTde AlvarengaSCA survey of intrusion detection in internet of thingsJ. Netw. Comput. Appl.2017842537 – reference: AlhajriRZagroubaRAl-HaidariFSurvey for anomaly detection of iot botnets using machine learning auto-encodersInt. J. Appl. Eng. Res.2019141024172421 – reference: Elhoseny, M.; Shankar, K.; Lakshmanaprabu, S.K.; Maseleno, A.; Arunkumar, N.: Hybrid optimization with cryptography encryption for medical image security in internet of things. Neural Comput. Appl. 1–15 (2018) – reference: CoelloCACPulidoGTHandling multiple objectives with particle swarm optimizationIEEE Trans. Evol. Comput.200483256279 – reference: AljarahIFarisHMirjaliliSAl-MadiNShetaAMafarjaMEvolving neural networks using bird swarm algorithm for data classification and regression applicationsCluster Comput.201922413171345 – reference: Mohemmed, A.W.; Zhang, M.: Evaluation of particle swarm optimization based centroid classifier with different distance metrics. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 2929–2932. IEEE (2008) – reference: LeeC-YYaoXEvolutionary programming using mutations based on the Lévy probability distributionIEEE Trans. Evol. Comput.200481113 – reference: Dheeru, D.; Taniskidou, E.K.: UCI machine learning repository (2017) – reference: da CostaKAPPapaJPLisboaCOMunozRde AlbuquerqueVHCInternet of things: a survey on machine learning-based intrusion detection approachesComput. Netw.2019151147157 – reference: Tama, B.A.; Rhee, K.-H.: An integration of PSO-based feature selection and random forest for anomaly detection in ToT network. In: MATEC Web of Conferences, vol. 159, pp. 01053. EDP Sciences (2018) – reference: SelvaraniPSureshAMalarvizhiNSecure and optimal authentication framework for cloud management using HGAPSO algorithmCluster Comput.201822240074016 – reference: LiSBiFChenWMiaoXLiuJTangCAn improved information security risk assessments method for cyber-physical-social computing and networkingIEEE Access201861031110319 – reference: MoustafaNTurnbullBChooK-KRAn ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of thingsIEEE Internet Things J.20186348154830 – reference: Alcalá-FdezJSanchezLGarciaSdel JesusMJVenturaSGarrellJMOteroJRomeroCBacarditJRivasVMKeel: a software tool to assess evolutionary algorithms for data mining problemsSoft Comput.2009133307318 – reference: AzmoodehADehghantanhaAContiMChooK-KRDetecting crypto-ransomware in iot networks based on energy consumption footprintJ. Ambient Intell. Humaniz. Comput.20189411411152 – reference: Heidari, A.A.; Aljarah, I.; Faris, H.; Chen, H.; Luo, J.; Mirjalili, S.: An enhanced associative learning-based exploratory whale optimizer for global optimization. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04015-0 – reference: LiDDengLLeeMWangHIoT data feature extraction and intrusion detection system for smart cities based on deep migration learningInt. J. Inf. Manag.201949533545 – reference: HanJPeiJKamberMData Mining: Concepts and Techniques2011AmsterdamElsevier1230.68018 – reference: Deb, K.; Agrawal, S.; Pratap, A.; Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In: International Conference on Parallel Problem Solving from Nature, pp. 849–858. Springer, Berlin (2000) – reference: FarisHAljarahIAl-MadiNMirjaliliSOptimizing the learning process of feedforward neural networks using lightning search algorithmInt. J. Artif. Intell. Tools20162561650033 – reference: SangeethaALBharathiNGaneshABRadhakrishnanTKParticle swarm optimization tuned cascade control system in an internet of things (IoT) environmentMeasurement20181178089 – reference: Heidari, A.A.; Faris, H.; Mirjalili, S.; Aljarah, I.; Mafarja, M.: Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks. In: Nature-Inspired Optimizers, pp. 23–46. Springer, Cham (2020) – reference: GehanEAA generalized Wilcoxon test for comparing arbitrarily singly-censored samplesBiometrika1965521–22032242071300133.41901 – reference: Sanchez-Pi, N.; Martí, L.; Molina, J.M.: Applying voreal for IoT intrusion detection. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 363–374. Springer (2018) – reference: Al Shorman, A.; Faris, H.; Aljarah, I.: Unsupervised intelligent system based on one class support vector machine and grey wolf optimization for iot botnet detection. J. Ambient Intell. Humaniz. Comput. 1–17 (2019) – reference: KesavamoorthyRSoundarKRSwarm intelligence based autonomous DDOS attack detection and defense using multi agent systemCluster Comput.201822494699476 – reference: DiroAAChilamkurtiNDistributed attack detection scheme using deep learning approach for internet of thingsFuture Gener. Comput. Syst.201882761768 – reference: Suman, C.; Tripathy, S.; Saha, S.: Building an effective intrusion detection system using unsupervised feature selection in multi-objective optimization framework. arXiv preprint arXiv:1905.06562 (2019) – reference: SunZZhangZXiaoCGangQDs evidence theory based trust ant colony routing in WSNChina Commun.20181532741 – reference: ChechkinAVMetzlerRKlafterJGoncharVYIntroduction to the theory of Lévy flightsAnomalous Transp. Found. Appl.2008492431451 – reference: Conti, M.; Dehghantanha, A.; Franke, K., Watson, S.: Internet of things security and forensics: challenges and opportunities (2018) – reference: Almomani, A.; Alweshah, M.; Al, S.: Metaheuristic algorithms-based feature selection approach for intrusion detection. In: Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices (2019) – reference: Yang, X.-S.; Metaheuristic optimization: algorithm analysis and open problems. In: International Symposium on Experimental Algorithms, pp. 21–32. Springer, Berlin (2011) – reference: RanaSHossainSShounHIAbul KashemMAn effective lightweight cryptographic algorithm to secure resource-constrained devicesInt. J. Adv. Comput. Sci. Appl.2018911267275 – reference: HallMFrankEHolmesGPfahringerBReutemannPWittenIHThe weka data mining software: an updateACM SIGKDD Explor. Newslett.20091111018 – reference: Corne, D.W; Jerram, N.R.; Knowles, J.D.; Oates, M.J.: Pesa-ii: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 283–290. Morgan Kaufmann Publishers Inc. (2001) – reference: ZhanyangXRenhaoGHuangTXiangHZhangXQiLXiaolongXAn IoT-oriented offloading method with privacy preservation for cloudlet-enabled wireless metropolitan area networksSensors20181893030 – reference: DebKKalyanmoyDMulti-objective Optimization Using Evolutionary Algorithms2001New YorkWiley0970.90091 – volume-title: Nature-Inspired Metaheuristic Algorithms year: 2010 ident: 4476_CR3 – volume: 82 start-page: 761 year: 2018 ident: 4476_CR23 publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2017.08.043 – volume: 49 start-page: 533 year: 2019 ident: 4476_CR24 publication-title: Int. J. Inf. Manag. doi: 10.1016/j.ijinfomgt.2019.04.006 – volume: 6 start-page: 2093 issue: 2 year: 2018 ident: 4476_CR45 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2883344 – ident: 4476_CR49 doi: 10.1007/s12652-019-01387-y – ident: 4476_CR1 doi: 10.1016/j.future.2017.07.060 – ident: 4476_CR16 – ident: 4476_CR35 – ident: 4476_CR10 doi: 10.1007/978-3-030-12127-3_3 – volume: 22 start-page: 4007 issue: 2 year: 2018 ident: 4476_CR43 publication-title: Cluster Comput. – volume: 8 start-page: 1 issue: 1 year: 2004 ident: 4476_CR60 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2003.816583 – volume: 8 start-page: 256 issue: 3 year: 2004 ident: 4476_CR50 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2004.826067 – volume: 15 start-page: 27 issue: 3 year: 2018 ident: 4476_CR44 publication-title: China Commun. doi: 10.1109/CC.2018.8331989 – volume: 11 start-page: 10 issue: 1 year: 2009 ident: 4476_CR63 publication-title: ACM SIGKDD Explor. Newslett. doi: 10.1145/1656274.1656278 – ident: 4476_CR48 doi: 10.1109/TENCON.2019.8929510 – ident: 4476_CR8 doi: 10.5220/0006048201710177 – volume-title: Data Mining: Concepts and Techniques year: 2011 ident: 4476_CR53 – volume: 117 start-page: 80 year: 2018 ident: 4476_CR39 publication-title: Measurement doi: 10.1016/j.measurement.2017.12.014 – volume: 46 start-page: 175 issue: 3 year: 1992 ident: 4476_CR54 publication-title: Am. Stat. doi: 10.1080/00031305.1992.10475879 – ident: 4476_CR12 doi: 10.1007/978-3-642-20662-7_2 – ident: 4476_CR47 doi: 10.1007/978-3-319-92639-1_30 – ident: 4476_CR32 – ident: 4476_CR9 doi: 10.1007/978-3-030-12127-3_8 – volume: 6 start-page: 10311 year: 2018 ident: 4476_CR46 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2800664 – volume: 17 start-page: 12 issue: 3 year: 2018 ident: 4476_CR28 publication-title: IEEE Pervasive Comput. doi: 10.1109/MPRV.2018.03367731 – ident: 4476_CR2 doi: 10.1007/978-3-319-70688-7_2 – ident: 4476_CR41 doi: 10.1051/matecconf/201815901053 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 4476_CR18 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2007.892759 – volume: 57 start-page: 2030 issue: 11–12 year: 2009 ident: 4476_CR57 publication-title: Comput. Math. Appl. doi: 10.1016/j.camwa.2008.09.023 – volume: 14 start-page: 2417 issue: 10 year: 2019 ident: 4476_CR29 publication-title: Int. J. Appl. Eng. Res. – volume: 52 start-page: 203 issue: 1–2 year: 1965 ident: 4476_CR62 publication-title: Biometrika doi: 10.1093/biomet/52.1-2.203 – volume: 35 start-page: 41 issue: 5 year: 2018 ident: 4476_CR22 publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2018.2825478 – volume: 22 start-page: 1317 issue: 4 year: 2019 ident: 4476_CR11 publication-title: Cluster Comput. doi: 10.1007/s10586-019-02913-5 – ident: 4476_CR30 doi: 10.1007/978-3-319-92058-0_56 – volume: 9 start-page: 267 issue: 11 year: 2018 ident: 4476_CR36 publication-title: Int. J. Adv. Comput. Sci. Appl. – ident: 4476_CR38 doi: 10.1155/2018/2492956 – volume: 84 start-page: 25 year: 2017 ident: 4476_CR19 publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2017.02.009 – ident: 4476_CR6 doi: 10.1109/AEECT.2015.7360576 – volume: 49 start-page: 431 issue: 2 year: 2008 ident: 4476_CR58 publication-title: Anomalous Transp. Found. Appl. – ident: 4476_CR25 doi: 10.1109/COMST.2020.2986444 – ident: 4476_CR14 – ident: 4476_CR56 – volume: 9 start-page: 1 year: 2013 ident: 4476_CR52 publication-title: Swarm Evolut. Comput. doi: 10.1016/j.swevo.2012.09.002 – volume: 9 start-page: 1141 issue: 4 year: 2018 ident: 4476_CR27 publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-017-0558-5 – ident: 4476_CR33 doi: 10.1201/9780429504044-8 – ident: 4476_CR21 doi: 10.1007/s10586-018-1847-2 – ident: 4476_CR34 doi: 10.1109/MASCOTS.2018.00038 – volume: 13 start-page: 307 issue: 3 year: 2009 ident: 4476_CR61 publication-title: Soft Comput. doi: 10.1007/s00500-008-0323-y – ident: 4476_CR55 doi: 10.1109/CEC.2008.4631192 – volume: 48 start-page: 445 year: 2018 ident: 4476_CR4 publication-title: Appl. Intell. doi: 10.1007/s10489-017-0967-3 – volume: 6 start-page: 4815 issue: 3 year: 2018 ident: 4476_CR20 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2871719 – volume: 151 start-page: 147 year: 2019 ident: 4476_CR26 publication-title: Comput. Netw. doi: 10.1016/j.comnet.2019.01.023 – volume: 25 start-page: 1650033 issue: 6 year: 2016 ident: 4476_CR5 publication-title: Int. J. Artif. Intell. Tools doi: 10.1142/S0218213016500330 – ident: 4476_CR7 doi: 10.1007/s00521-019-04015-0 – ident: 4476_CR51 – ident: 4476_CR17 – volume: 72 start-page: 79 year: 2018 ident: 4476_CR31 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.05.049 – ident: 4476_CR40 doi: 10.1007/s00521-018-3801-x – volume: 18 start-page: 3030 issue: 9 year: 2018 ident: 4476_CR37 publication-title: Sensors doi: 10.3390/s18093030 – volume-title: Applied Predictive Modeling year: 2013 ident: 4476_CR59 doi: 10.1007/978-1-4614-6849-3 – volume-title: Multi-objective Optimization Using Evolutionary Algorithms year: 2001 ident: 4476_CR13 – ident: 4476_CR15 doi: 10.1007/3-540-45356-3_83 – volume: 22 start-page: 9469 issue: 4 year: 2018 ident: 4476_CR42 publication-title: Cluster Comput.
SSID	ssib048395113 ssj0001916267 ssj0061873
Score	2.3350205
Snippet	The emerging of the Internet of things (IoT), and more, the advent of the Internet of everything have revolutionized the computer networks industry. The high...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	6081
SubjectTerms	Authentication Computer networks Encryption Engineering Evolutionary algorithms Humanities and Social Sciences Internet of Things Intrusion detection systems multidisciplinary Multiple objective analysis Network security Particle swarm optimization Protocol (computers) Research Article-Computer Engineering and Computer Science Science
Title	A Modified Multi-objective Particle Swarm Optimizer-Based Lévy Flight: An Approach Toward Intrusion Detection in Internet of Things
URI	https://link.springer.com/article/10.1007/s13369-020-04476-9 https://www.proquest.com/docview/2425623989
Volume	45
WOSCitedRecordID	wos000522934600001&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: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 2191-4281 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001916267 issn: 2193-567X databaseCode: RSV dateStart: 20110101 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/eLvHCXMwnV3JSgQxEA1uBz24izt18KaBttNbvI3LoOAGLsytSWeBkbFHZgZFz_6M3-GPWUl32yoqKPQtC02qknqVVL0iZCPzZGhCrSnTQtBAcWYfCRX1tYy18FWcbUtXbCI-PU1aLX5eJoX1q2j36knSndR1shtjEafW3fGCII4oHyajoWWbsT76xXWlRQGafEQRrL5pQQTku1KyuDsZDaO4VWbPfD_tZwtVw84vL6XOADWn_vfr02SyBJzQKDRkhgzpfJZMfKAhnCPPDTjpqrZBNAouIZd2s5viIITzUrXg4kH0buEMT5jb9pPu0V00fwqOX1_uH6HZsS7-DjRyaJQc5XDp4nHhKLdZHSh82NcDF_aVQzuH4iJSD6BroCgdOk-umgeXe4e0rM5AJW7bAY3Q9AmRMU9KlLfKQuNlIX4IYNBB54gcNIu1yqThyngJT7C_iRNh6UDRC5RsgYzk3VwvElA-DotNiGhEBr4wIsEeXCiT-FkShcES2a4kksqSutxW0OikNemyXeEUVzh1K5zyJbL5PuauIO74tfdqJei03MT91HpjkeVHxOatSrB188-zLf-t-woZ951u2LDCVTKCctFrZEzeD9r93rpT7jfnVPKH
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LTxsxELYoVCocCuUh0vKYQ2_U0rLel7mFRwQiBCQCym3l9UMKgg1KoiA482f4Hf1jjL27bEEUqZX25rG18ow933hehPzMPBmaUGvKtBA0UJxZJ6GivpaxFr6Ks23pmk3EnU7S6_GzMilsVEW7Vy5Jd1PXyW6MRZxac8cLgjii_BOZCWybHWujn19WUhSgykcUweqXFkRAvmsli6eT0TCKe2X2zPvLvtZQNex84yl1Cqg1_3-_vkC-loATmoWEfCNTOl8kc3-UIVwij004Gai-QTQKLiGXDrKr4iKEs1K04PxODG_gFG-Ym_6DHtJdVH8K2r-fJvfQurYm_g40c2iWNcqh6-Jx4Si3WR3IfNjXYxf2lUM_h-IhUo9hYKBoHbpMLloH3b1DWnZnoBKP7ZhGqPqEyJgnJfJbZaHxshA_BDBooHNEDprFWmXScGW8hCdIb-JE2HKgaAVKtkKm80GuVwkoH6fFJkQ0IgNfGJEgBRfKJH6WRGHQINsVR1JZli63HTSu07rost3hFHc4dTuc8gbZeplzWxTu-JB6rWJ0Wh7iUWqtscjWR8ThXxVj6-G_r_b938g3yZfD7kk7bR91jn-QWd_JiQ0xXCPTyCO9Tj7Lybg_Gm44QX8GTQf1aw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwELaAItQeKBRQeZU5cAOLbJyXe9sWVkXAsuKlvUWOH9IiyKIlomrP_Jn-Dv4YYychgAoSQsrNDymesecbe-YbQtYzT4Ym1JoyLQQNFGf2kVBRX8tYC1_FWUu6YhNxt5v0-7z3KIvfRbvXT5JlToNlacqLrStltprEN8YiTq3r4wVBHFE-Tj4E6MnYoK6j47NaowI0_4goWHPrgmjId2VlcacyGkZxv8qk-f-0T61VA0GfvZo6Y9T5_P7fmCHTFRCFdqk5s2RM51_Ip0f0hHPktg0HQzUwiFLBJerSYXZeHpDQq1QOjn-L0SUc4slzOfirR_QHmkUF-3f_bv5A58K6_t-hnUO74i6HExenC7u5zfZApYBtXbhwsBwGOZQXlLqAoYGypOg8Oe3snPz8RauqDVTidi5ohCZRiIx5UqIeqCw0Xhbih8AGHXeOiEKzWKtMGq6Ml_AE-5s4EZYmFL1DyRbIRD7M9VcCysdhsQkRpcjAF0Yk2IMLZRI_S6IwWCStWjqprCjNbWWNi7QhY7YrnOIKp26FU75INh7GXJWEHq_2XqmFnlab-zq1XlpkeROxebMWctP88mxLb-u-RqZ62510f7e7t0w--k5NbOThCplAEelVMilvisH16JvT-Xv4Ef5P
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=A+Modified+Multi-objective+Particle+Swarm+Optimizer-Based+L%C3%A9vy+Flight%3A+An+Approach+Toward+Intrusion+Detection+in+Internet+of+Things&rft.jtitle=The+Arabian+Journal+for+Science+and+Engineering.+Section+B%2C+Engineering&rft.au=Habib%2C+Maria&rft.au=Aljarah+Ibrahim&rft.au=Faris+Hossam&rft.date=2020-08-01&rft.pub=Springer+Nature+B.V&rft.issn=1319-8025&rft.eissn=2191-4281&rft.volume=45&rft.issue=8&rft.spage=6081&rft.epage=6108&rft_id=info:doi/10.1007%2Fs13369-020-04476-9&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2193-567X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2193-567X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2193-567X&client=summon