Modified graph-based algorithm to analyze security threats in IoT

In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, s...

Full description

Saved in:

Bibliographic Details
Published in:	PeerJ. Computer science Vol. 9; p. e1743
Main Authors:	Arat, Ferhat, Akleylek, Sedat
Format:	Journal Article
Language:	English
Published:	United States PeerJ. Ltd 08.12.2023 PeerJ, Inc PeerJ Inc
Subjects:	Algorithms Algorithms and Analysis of Algorithms Analysis Control systems Controllers Cybersecurity Embedded systems Graph theory Industrial applications Industrial electronics Industrial IoT Information technology Internet of Things Interoperability Manufacturing Physical instruments Risk assessment Safety and security measures Security Security and Privacy Security systems Sensors Technology application Threat evaluation Threat graph Threats Threats Graph theory Security Industrial IoT Risk assessment Threat graph
ISSN:	2376-5992, 2376-5992
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained.
AbstractList	In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained. In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained. In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained.In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained. In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained.
ArticleNumber	e1743
Audience	Academic
Author	Akleylek, Sedat Arat, Ferhat
Author_xml	– sequence: 1 givenname: Ferhat orcidid: 0000-0002-4347-0016 surname: Arat fullname: Arat, Ferhat organization: Department of Software Engineering, Samsun University, Samsun, Turkey – sequence: 2 givenname: Sedat orcidid: 0000-0001-7005-6489 surname: Akleylek fullname: Akleylek, Sedat organization: Department of Computer Engineering, Ondokuz Mayis University Samsun, Samsun, Turkey, University of Tartu, Tartu, Estonia, Cyber Security and Information Technologies Research and Development Centre, Ondokuz Mayis University Samsun, Samsun, Turkey
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38192462$$D View this record in MEDLINE/PubMed
BookMark	eNp1kktv1DAUhSNUREvpki2KxAYWGfxI_FihUcVjpCIkKGvrxrnJeJSJBztBDL8eT6eUpgJ7Yev6u8fy8XmanQx-wCx7TslCSirf7BDDprBxQWXJH2VnjEtRVFqzk3v70-wixg0hhFY0Df0kO-WKalYKdpYtP_nGtQ6bvAuwWxc1xLSHvvPBjettPvocBuj3vzCPaKdU3OfjOiCMMXdDvvLXz7LHLfQRL27X8-zb-3fXlx-Lq88fVpfLq8JWvCyL2hJVCc5B8FI3tW5rKZC3SLkE3vK6qVgFtlSWCytsTYAp0oBWKBoCGpCfZ6ujbuNhY3bBbSHsjQdnbgo-dAbC6GyPhoqWQqVoEmtLK5SqOFLRIFGtBEnrpPX2qLWb6i02FocxQD8TnZ8Mbm06_8NQIiXXTCSFV7cKwX-fMI5m66LFvocB_RQN05RVLP2KTOjLB-jGTyGZmiildUUJF_Qv1UF6gRtany62B1GzVJTrklFWJmrxDyrNBrfOpnC0LtVnDa9nDYkZ8efYwRSjWX39Mmdf3Hflzo4_aUlAcQRs8DEGbO8QSswhkOYmkMZGcwhk4vkD3roRRucPprr-P12_Aeo14wg
CitedBy_id	crossref_primary_10_1016_j_comnet_2025_111122 crossref_primary_10_1109_ACCESS_2025_3558985
Cites_doi	10.1109/TII.2018.2808190 10.2478/amcs-2014-0016 10.1109/TDSC.2011.34 10.1155/2022/1401683 10.1016/j.cose.2021.102316 10.2139/ssrn.3603739 10.1109/MCOM.2017.1600528 10.1109/TII.2022.3152814 10.1109/COMST.2022.3158270 10.1016/j.jmsy.2020.10.011 10.1016/j.neucom.2021.07.101 10.1109/TII.2018.2832853 10.1016/j.comnet.2023.110046 10.1016/j.compind.2018.04.015 10.4018/978-1-7998-2466-4.ch038 10.1109/TII.2014.2300753 10.1016/j.csi.2017.09.006 10.1016/j.sintl.2021.100121 10.1016/j.scs.2020.102343 10.1109/ACCESS.2018.2863244 10.1109/ACCESS.2018.2805690 10.1016/j.cose.2023.103174 10.1016/j.adhoc.2021.102558
ContentType	Journal Article
Copyright	2023 Arat and Akleylek. COPYRIGHT 2023 PeerJ. Ltd. 2023 Arat and Akleylek. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 Arat and Akleylek 2023 Arat and Akleylek
Copyright_xml	– notice: 2023 Arat and Akleylek. – notice: COPYRIGHT 2023 PeerJ. Ltd. – notice: 2023 Arat and Akleylek. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2023 Arat and Akleylek 2023 Arat and Akleylek
DBID	AAYXX CITATION NPM ISR 3V. 7XB 8AL 8FE 8FG 8FK ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- M0N P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI Q9U 7X8 5PM DOA
DOI	10.7717/peerj-cs.1743
DatabaseName	CrossRef PubMed Gale In Context: Science ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Local Electronic Collection Information ProQuest Central Technology Collection ProQuest One Community College ProQuest Central Korea ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Computing Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies & Aerospace Collection ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	CrossRef Publicly Available Content Database MEDLINE - Academic PubMed
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	2376-5992
ExternalDocumentID	oai_doaj_org_article_16f1a5818c3f4c68853e16de08f7a71b PMC10773926 A813942124 38192462 10_7717_peerj_cs_1743
Genre	Journal Article
GrantInformation_xml	– fundername: ASELSAN A.Ş
GroupedDBID	53G 5VS 8FE 8FG AAFWJ AAYXX ABUWG ADBBV AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS ARAPS ARCSS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO FRP GNUQQ GROUPED_DOAJ HCIFZ IAO ICD IEA ISR ITC K6V K7- M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC RPM 3V. H13 M0N NPM 7XB 8AL 8FK JQ2 PKEHL PQEST PQUKI Q9U 7X8 5PM
ID	FETCH-LOGICAL-c5344-bc085633a6349db9fb76e3fe137a3f3bd525ac48c36c6cb0a280da98e6d0a9ae3
IEDL.DBID	DOA
ISSN	2376-5992
IngestDate	Fri Oct 03 12:52:31 EDT 2025 Tue Nov 04 02:06:19 EST 2025 Sun Nov 09 09:37:34 EST 2025 Sat Jul 26 00:09:16 EDT 2025 Tue Nov 11 10:54:16 EST 2025 Tue Nov 04 18:14:24 EST 2025 Thu Nov 13 16:10:45 EST 2025 Thu Jan 02 22:29:51 EST 2025 Tue Nov 18 21:26:01 EST 2025 Sat Nov 29 05:31:07 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Threats Graph theory Security Industrial IoT Risk assessment Threat graph
Language	English
License	https://creativecommons.org/licenses/by/4.0 2023 Arat and Akleylek. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c5344-bc085633a6349db9fb76e3fe137a3f3bd525ac48c36c6cb0a280da98e6d0a9ae3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0001-7005-6489 0000-0002-4347-0016
OpenAccessLink	https://doaj.org/article/16f1a5818c3f4c68853e16de08f7a71b
PMID	38192462
PQID	2899510361
PQPubID	2045934
PageCount	e1743
ParticipantIDs	doaj_primary_oai_doaj_org_article_16f1a5818c3f4c68853e16de08f7a71b pubmedcentral_primary_oai_pubmedcentral_nih_gov_10773926 proquest_miscellaneous_2912527437 proquest_journals_2899510361 gale_infotracmisc_A813942124 gale_infotracacademiconefile_A813942124 gale_incontextgauss_ISR_A813942124 pubmed_primary_38192462 crossref_primary_10_7717_peerj_cs_1743 crossref_citationtrail_10_7717_peerj_cs_1743
PublicationCentury	2000
PublicationDate	2023-12-08
PublicationDateYYYYMMDD	2023-12-08
PublicationDate_xml	– month: 12 year: 2023 text: 2023-12-08 day: 08
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: San Diego – name: San Diego, USA
PublicationTitle	PeerJ. Computer science
PublicationTitleAlternate	PeerJ Comput Sci
PublicationYear	2023
Publisher	PeerJ. Ltd PeerJ, Inc PeerJ Inc
Publisher_xml	– name: PeerJ. Ltd – name: PeerJ, Inc – name: PeerJ Inc
References	Polatidis (10.7717/peerj-cs.1743/ref-17) 2018; 56 Mouratidis (10.7717/peerj-cs.1743/ref-14) 2018b; 14 Mosteiro-Sanchez (10.7717/peerj-cs.1743/ref-12) 2020; 57 Stellios (10.7717/peerj-cs.1743/ref-22) 2021; 107 Jaidka (10.7717/peerj-cs.1743/ref-9) 2020 Qureshi (10.7717/peerj-cs.1743/ref-21) 2020; 61 Nguyen (10.7717/peerj-cs.1743/ref-16) 2022; 18 CVE (10.7717/peerj-cs.1743/ref-6) 2023 Javaid (10.7717/peerj-cs.1743/ref-10) 2021; 2 Mouratidis (10.7717/peerj-cs.1743/ref-13) 2018a; 14 Wang (10.7717/peerj-cs.1743/ref-26) 2018; 6 Sukiasyan (10.7717/peerj-cs.1743/ref-23) 2022; 484 Pretorius (10.7717/peerj-cs.1743/ref-19) 2020 Szwed (10.7717/peerj-cs.1743/ref-25) 2014; 24 Al-Turjman (10.7717/peerj-cs.1743/ref-1) 2018; 14 Jing (10.7717/peerj-cs.1743/ref-11) 2022; 2022 Da Xu (10.7717/peerj-cs.1743/ref-7) 2014; 10 Nandhini (10.7717/peerj-cs.1743/ref-15) 2019 Wu (10.7717/peerj-cs.1743/ref-27) 2022; 24 Arat (10.7717/peerj-cs.1743/ref-3) 2023b; 237 Boyes (10.7717/peerj-cs.1743/ref-4) 2018; 101 Arat (10.7717/peerj-cs.1743/ref-2) 2023a; 128 Prostov (10.7717/peerj-cs.1743/ref-20) 2021 Brewster (10.7717/peerj-cs.1743/ref-5) 2017; 55 Sun (10.7717/peerj-cs.1743/ref-24) 2021; 120 George (10.7717/peerj-cs.1743/ref-8) 2018; 6 Poolsappasit (10.7717/peerj-cs.1743/ref-18) 2012; 9
References_xml	– start-page: 242 year: 2019 ident: 10.7717/peerj-cs.1743/ref-15 article-title: Directed acyclic graph inherited attacks and mitigation methods in RPL: a review – volume: 14 start-page: 2736 issue: 6 year: 2018 ident: 10.7717/peerj-cs.1743/ref-1 article-title: Context-sensitive access in industrial internet of things (IIoT) healthcare applications publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2018.2808190 – volume: 24 start-page: 213 issue: 1 year: 2014 ident: 10.7717/peerj-cs.1743/ref-25 article-title: A new lightweight method for security risk assessment based on fuzzy cognitive maps publication-title: International Journal of Applied Mathematics and Computer Science doi: 10.2478/amcs-2014-0016 – volume: 9 start-page: 61 issue: 1 year: 2012 ident: 10.7717/peerj-cs.1743/ref-18 article-title: Dynamic security risk management using Bayesian attack graphs publication-title: IEEE Transactions on Dependable and Secure Computing doi: 10.1109/TDSC.2011.34 – volume: 2022 start-page: 1 issue: 1–2 year: 2022 ident: 10.7717/peerj-cs.1743/ref-11 article-title: Detection of DDoS attack within industrial IoT devices based on clustering and graph structure features publication-title: Security and Communication Networks doi: 10.1155/2022/1401683 – volume: 107 start-page: 102316 issue: 2 year: 2021 ident: 10.7717/peerj-cs.1743/ref-22 article-title: Assessing IoT enabled cyber-physical attack paths against critical systems publication-title: Computers & Security doi: 10.1016/j.cose.2021.102316 – year: 2020 ident: 10.7717/peerj-cs.1743/ref-9 article-title: Evolution of IoT to IIoT: applications & challenges doi: 10.2139/ssrn.3603739 – volume: 55 start-page: 26 issue: 9 year: 2017 ident: 10.7717/peerj-cs.1743/ref-5 article-title: IoT in agriculture: designing a Europe-wide large-scale pilot publication-title: IEEE Communications Magazine doi: 10.1109/MCOM.2017.1600528 – volume: 18 start-page: 1 issue: 11 year: 2022 ident: 10.7717/peerj-cs.1743/ref-16 article-title: An advanced computing approach for IoT-botnet detection in industrial internet of things publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2022.3152814 – volume: 24 start-page: 1175 issue: 2 year: 2022 ident: 10.7717/peerj-cs.1743/ref-27 article-title: A survey of intelligent network slicing management for industrial IoT: integrated approaches for smart transportation, smart energy, and smart factory publication-title: IEEE Communications Surveys & Tutorials doi: 10.1109/COMST.2022.3158270 – volume: 57 start-page: 367 issue: 5 year: 2020 ident: 10.7717/peerj-cs.1743/ref-12 article-title: Securing IIoT using defence-in-depth: towards an end-to-end secure industry 4.0 publication-title: Journal of Manufacturing Systems doi: 10.1016/j.jmsy.2020.10.011 – volume: 484 start-page: 183 issue: 4 year: 2022 ident: 10.7717/peerj-cs.1743/ref-23 article-title: Secure data exchange in industrial internet of things publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.07.101 – volume: 14 start-page: 4093 issue: 9 year: 2018b ident: 10.7717/peerj-cs.1743/ref-14 article-title: A security analysis method for industrial internet of things publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2018.2832853 – volume: 237 start-page: 110046 year: 2023b ident: 10.7717/peerj-cs.1743/ref-3 article-title: A new method for vulnerability and risk assessment of IoT publication-title: Computer Networks doi: 10.1016/j.comnet.2023.110046 – volume: 101 start-page: 1 issue: 8 year: 2018 ident: 10.7717/peerj-cs.1743/ref-4 article-title: The industrial internet of things (IIoT): an analysis framework publication-title: Computers in Industry doi: 10.1016/j.compind.2018.04.015 – start-page: 613 volume-title: Cyber Warfare and Terrorism: Concepts, Methodologies, Tools, and Applications year: 2020 ident: 10.7717/peerj-cs.1743/ref-19 article-title: Cyber-security for ICS/SCADA: a South African perspective doi: 10.4018/978-1-7998-2466-4.ch038 – start-page: 2394 year: 2021 ident: 10.7717/peerj-cs.1743/ref-20 article-title: Construction and security analysis of private directed acyclic graph based systems for internet of things – volume: 10 start-page: 2233 issue: 4 year: 2014 ident: 10.7717/peerj-cs.1743/ref-7 article-title: Internet of things in industries: a survey publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2014.2300753 – volume: 14 start-page: 4093 issue: 9 year: 2018a ident: 10.7717/peerj-cs.1743/ref-13 article-title: A security analysis method for industrial internet of things publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2018.2832853 – volume: 56 start-page: 74 year: 2018 ident: 10.7717/peerj-cs.1743/ref-17 article-title: Cyber-attack path discovery in a dynamic supply chain maritime risk management system publication-title: Computer Standards & Interfaces doi: 10.1016/j.csi.2017.09.006 – volume: 2 start-page: 100121 issue: 12 year: 2021 ident: 10.7717/peerj-cs.1743/ref-10 article-title: Sensors for daily life: a review publication-title: Sensors International doi: 10.1016/j.sintl.2021.100121 – volume: 61 start-page: 102343 issue: 1 year: 2020 ident: 10.7717/peerj-cs.1743/ref-21 article-title: A novel and secure attacks detection framework for smart cities industrial internet of things publication-title: Sustainable Cities and Society doi: 10.1016/j.scs.2020.102343 – volume: 6 start-page: 43586 year: 2018 ident: 10.7717/peerj-cs.1743/ref-8 article-title: A graph-based security framework for securing industrial IoT networks from vulnerability exploitations publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2863244 – volume: 6 start-page: 8599 year: 2018 ident: 10.7717/peerj-cs.1743/ref-26 article-title: A vulnerability assessment method in industrial internet of things based on attack graph and maximum flow publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2805690 – volume: 128 start-page: 103174 issue: 5 year: 2023a ident: 10.7717/peerj-cs.1743/ref-2 article-title: Attack path detection for IIoT enabled cyber physical systems: revisited publication-title: Computers & Security doi: 10.1016/j.cose.2023.103174 – volume: 120 start-page: 102558 issue: 8 year: 2021 ident: 10.7717/peerj-cs.1743/ref-24 article-title: Effective malware detection scheme based on classified behavior graph in IIoT publication-title: Ad Hoc Networks doi: 10.1016/j.adhoc.2021.102558 – year: 2023 ident: 10.7717/peerj-cs.1743/ref-6 article-title: Common Vulnerabilities and Exposures
SSID	ssj0001511119
Score	2.322105
Snippet	In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these...
SourceID	doaj pubmedcentral proquest gale pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	e1743
SubjectTerms	Algorithms Algorithms and Analysis of Algorithms Analysis Control systems Controllers Cybersecurity Embedded systems Graph theory Industrial applications Industrial electronics Industrial IoT Information technology Internet of Things Interoperability Manufacturing Physical instruments Risk assessment Safety and security measures Security Security and Privacy Security systems Sensors Technology application Threat evaluation Threat graph Threats
SummonAdditionalLinks	– databaseName: Advanced Technologies & Aerospace Database dbid: P5Z link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagcOBCeRMoyCAEF6ImsePHCS2Iih6oKihSxcWyHXt3UZtsN1kO_Ho8Xu-2EYIL13gS2Z6HP9uTbxB6JZxm3EqXe8_DBoU6nWsR_JELXRraaM2FicUm-NGROD2Vx-nArU9plZuYGAN101k4I9-HjQGwv7Hy3eIih6pRcLuaSmhcRzeAJQFKNxzX3y_PWGoICHJNrcnDxmV_4dzyR257yOMho6UoMvb_GZevLEzjpMkrq9DB7v_2_w66nfAnnqwN5i665tp7aHdT2wEnV7-PJp-7Zu4DPsWR0jqH1a7B-mwavjnMzvHQYQ18Jr8c7lMJPDzMAIH2eN7iw-7kAfp28PHkw6c8lVvIbU0ozY0N8IsRohmhsjHSG84c8a4kXBNPTFNXtbZUWMIss6bQlSgaLYVjTQEc3-Qh2mm71j1GWFAJRPY6OLuhpuRGEiKJNoZ7UxvvM_R2M_PKJi5yKIlxpsKeBBSloqKU7RUoKkOvt-KLNQnH3wTfgxq3QsCdHR90y6lKrqhK5ktdB6BiiaeWiQBYXMkaVwgfelyaDL0EI1DAjtFC-s1Ur_peHX79oiYiAGa4Q6cZepOEfBd6bnX6myGMHwi1RpJ7I8ngvnbcvLEXlcJHry6NJUMvts3wJqTEta5bBRkZsGkVBs0z9GhtmttxR5o7yqoMiZHRjiZm3NLOZ5FcvCw4D5iZPfl3v56iW1WAezGxR-yhnWG5cs_QTftzmPfL59ENfwNSxD76 priority: 102 providerName: ProQuest
Title	Modified graph-based algorithm to analyze security threats in IoT
URI	https://www.ncbi.nlm.nih.gov/pubmed/38192462 https://www.proquest.com/docview/2899510361 https://www.proquest.com/docview/2912527437 https://pubmed.ncbi.nlm.nih.gov/PMC10773926 https://doaj.org/article/16f1a5818c3f4c68853e16de08f7a71b
Volume	9
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: K7- dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: P5Z dateStart: 20150527 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: BENPR dateStart: 20150527 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: PIMPY dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwELZg4cCF5blkWaqAEFyItqkdP45d1BUValUti1S4WLZjb4uWZNW0HDjw25lJ0qoRQly4-BBPpPjzjGdGGX9DyGvpDRdO-SQEAQkK8yYxEuxRSJNalhsjpK2bTYjpVM7narbX6gtrwhp64Aa405SH1GTgVhwNzHEJ7sWnPPd9GYQRqcXTty_UXjLV3A_Go0A1pJoCUpbTG-9X3xJXYQUP7Tihmqv_zxN5zyV1yyX3_M_5A3K_DRzjYfPBD8ktXzwih9umDHFro4_JcFLmywCBZVxzUSfopvLYXF-Vq-V68T1el7FBIpKfPq7a3nXxeoGhYxUvi3hcXj4hn89Hl-8_JG2fhMRllLHEOoibOKWGU6Zyq4IV3NPgUyoMDdTm2SAzjgGC3HFn-2Yg-7lR0vO8j-Tc9Ck5KMrCPyOxZAoZ6A1YqWU2FVZRqqixVgSb2RAi8m4LnHYtiTj2srjWkEwgzrrGWbtKI84RebMTv2nYM_4meIa7sBNC0uv6AaiCblVB_0sVIvIK91AjrUWBdTNXZlNVevzpQg8lRLr485tF5G0rFEr4cmfaawiwfmTC6kiedCTB7lx3eqsqurX7SmP6ihyFPI3Iy900vom1bIUvNyCjIKgcwKJFRI4azdqtu-anY3wQEdnRuQ4w3ZliuahZwSGPFxDs8uP_AeVzcm8A0VxdtyNPyMF6tfEvyF33Y72sVj1yW8xlj9w5G01nF73a8mD8KBIYJ79GMM6yrzA_G09mX34DjiA3zw
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQYIL5U2ggEE8LkTdxIntHBBaHlVX264QFKk3Yzv27qKSLJtdEPwofiMz2WTbCMGtB67xJPIkM9_MJJNvCHksnebCZi70XkCBkjgdagn-KKSOTJJrLaSph02I0UgeHWXvNsiv9l8YbKtsMbEG6ry0-I58BwsDZH_j0cvZ1xCnRuHX1XaExsoshu7HdyjZqheDN_B8n8Tx7tvD13thM1UgtClLktBYyDI4Y5qzJMtN5o3gjnkXMaGZZyZP41TbRFrGLbemp2PZy3UmHc97SGXN4LrnyPmESYF-NRThyTudFAEoW1F5CiiUdmbOzT-HtsK-IdYJffWEgD_jwKlA2G3SPBX1drf-t_t1hVxu8mvaXznEVbLhimtkq51dQRsou076B2U-9ZB_05qyO8RonlN9PAYdFpMvdFFSjXwtPx2tmhF_dDHBDLui04IOysMb5OOZKHKTbBZl4W4TKpMMifo1gJlJTCRMxljGtDHCm9R4H5Dn7ZNWtuFax5EfxwpqLjQMVRuGspVCwwjI07X4bEUy8jfBV2g2ayHkBq8PlPOxaqBGRdxHOoVEzDKfWC4hIXMRz11PethxZALyCI1OIftHge1FY72sKjX48F71JRQE2COQBORZI-RL2LnVzd8aoD8ShnUktzuSAE-2u9zap2rgsVInxhmQh-tlPBNb_gpXLkEmg9w7BqVFQG6tXGGtd03jl_A4ILLjJJ0b010pppOaPD3qCQE1Ab_z7309IBf3Dg_21f5gNLxLLsWQ2tZNTHKbbC7mS3ePXLDfFtNqfr-GAEo-nbUP_QbqQZy0
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lj9MwELaWBSEuLG8CCxjE40LUJk5s54BQYamoFqoKFmnFxdiO3RYtSWlaEPw0fh0zadrdCMFtD1zjSeRJvnklk28IeSid5sJmLvReQIGSOB1qCfYopI5MkmstpKmHTYjhUB4eZqMt8mv9Lwy2Va59Yu2o89LiO_IOFgbI_sajjm_aIkZ7_eezryFOkMIvretxGiuI7Lsf36F8q54N9uBZP4rj_quDl6_DZsJAaFOWJKGxkHFwxjRnSZabzBvBHfMuYkIzz0yexqm2ibSMW25NV8eym-tMOp53kdaawXXPkLMCakxsJxylH4_f76TojLIVraeAoqkzc27-ObQV9hCxVhispwX8GRNOBMV2w-aJCNjf-Z_v3SVyscm7aW9lKJfJliuukJ31TAvauLirpPe2zKce8nJaU3mHGOVzqo_GoMNi8oUuSqqRx-Wno1Uz-o8uJph5V3Ra0EF5cI18OBVFrpPtoizcTUJlkiGBvwYnZxITCZMxljFtjPAmNd4H5On6qSvbcLDjKJAjBbUYgkTVIFG2UgiSgDzeiM9W5CN_E3yBENoIIWd4faCcj1XjglTEfaRTSNAs84nlEhI1F_HcdaWHHUcmIA8QgApZQQrEyFgvq0oN3r9TPQmFAvYOJAF50gj5EnZudfMXB-iPRGItyd2WJLgt215eY1U1brNSx0ANyP3NMp6JrYCFK5cgk0FOHoPSIiA3Vmax0bum90t4HBDZMpjWjWmvFNNJTaoedYWAWoHf-ve-7pHzYDrqzWC4f5tciCHjrXub5C7ZXsyX7g45Z78tptX8bu0NKPl02ib0GyVBpdg
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=Modified+graph-based+algorithm+to+analyze+security+threats+in+IoT&rft.jtitle=PeerJ.+Computer+science&rft.au=Arat%2C+Ferhat&rft.au=Akleylek%2C+Sedat&rft.date=2023-12-08&rft.pub=PeerJ.+Ltd&rft.issn=2376-5992&rft.eissn=2376-5992&rft.volume=9&rft.spage=e1743&rft_id=info:doi/10.7717%2Fpeerj-cs.1743&rft.externalDocID=A813942124
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2376-5992&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2376-5992&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2376-5992&client=summon