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

Ecevit, M.I.Çukur, Z.Izgün, M.A.Ui Ain, N.Daǧ, H.2025-02-152025-02-15202409798350365887https://doi.org/10.1109/UBMK63289.2024.10773407https://hdl.handle.net/20.500.12469/7195The 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.eninfo:eu-repo/semantics/closedAccessCyber SecurityEdge IiotEnsemble LearningResource-Efficient Machine LearningResource-Efficient Ensemble Learning for Edge Iiot Network Security Against Osint-Based AttacksConference Object77878310.1109/UBMK63289.2024.107734072-s2.0-85215524083N/AN/A