Resource-Efficient Ensemble Learning for Edge Iiot Network Security Against Osint-Based Attacks

dc.authorscopusid 57964038500
dc.authorscopusid 59520347900
dc.authorscopusid 59521007300
dc.authorscopusid 57437833000
dc.authorscopusid 6507328166
dc.contributor.author Ecevit, Mert İlhan
dc.contributor.author Çukur, Z.
dc.contributor.author Izgün, M.A.
dc.contributor.author Ui Ain, N.
dc.contributor.author Daǧ, H.
dc.contributor.other Management Information Systems
dc.date.accessioned 2025-02-15T19:38:33Z
dc.date.available 2025-02-15T19:38:33Z
dc.date.issued 2024
dc.department Kadir Has University en_US
dc.department-temp Ecevit M.I., Kadir Has University, CCIP, Center for Cyber Security and Critical Infrastructure Protection, Istanbul, Turkey; Çukur Z., Kadir Has University, Department of Management Information Systems, Istanbul, Turkey; Izgün M.A., Kadir Has University, Department of Management Information Systems, Istanbul, Turkey; Ui Ain N., Kadir Has University, Department of Management Information Systems, Istanbul, Turkey; Daǧ H., Kadir Has University, CCIP, Center for Cyber Security and Critical Infrastructure Protection, Istanbul, Turkey en_US
dc.description.abstract The rise of Edge IIoT networks has transformed industries by enabling real-time data processing, but these networks face significant c ybersecurity risks, particularly from OSINT-based attacks. This paper presents a resource-efficient ensemble learning framework designed to detect such attacks in Edge IIoT environments. The framework integrates machine learning models, including RandomForest, K-Nearest Neighbors, and Logistic Regression, optimized with Principal Component Analysis (PCA) to reduce data dimensionality and computational overhead. GridSearchCV and StratifiedKFold cross-validation were employed to fine-tune the models, resulting in high detection accuracy. This approach ensures robust and efficient security for resource-constrained Edge IIoT networks. © 2024 IEEE. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1109/UBMK63289.2024.10773407
dc.identifier.endpage 783 en_US
dc.identifier.isbn 9798350365887
dc.identifier.scopus 2-s2.0-85215524083
dc.identifier.scopusquality N/A
dc.identifier.startpage 778 en_US
dc.identifier.uri https://doi.org/10.1109/UBMK63289.2024.10773407
dc.identifier.uri https://hdl.handle.net/20.500.12469/7195
dc.identifier.wosquality N/A
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof UBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineering -- 9th International Conference on Computer Science and Engineering, UBMK 2024 -- 26 October 2024 through 28 October 2024 -- Antalya -- 204906 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 0
dc.subject Cyber Security en_US
dc.subject Edge Iiot en_US
dc.subject Ensemble Learning en_US
dc.subject Resource-Efficient Machine Learning en_US
dc.title Resource-Efficient Ensemble Learning for Edge Iiot Network Security Against Osint-Based Attacks en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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