Fuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in Vehicular Ad-hoc Networks

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dc.authoridAlbahri, A.S./0000-0003-3335-457X
dc.authoridAlbahrey, Osamah Shihab/0000-0002-7844-3990
dc.authoridAlzubaidi, Laith/0000-0002-7296-5413
dc.authoridSantamaria, Jose/0000-0002-2022-6838
dc.authoridGu, Yuantong/0000-0002-2770-5014
dc.authorwosidGU, Yuantong/C-5033-2009
dc.authorwosidAlbahri, A.S./E-7428-2018
dc.authorwosidAlbahrey, Osamah Shihab/D-5150-2018
dc.authorwosidAlzubaidi, Laith/AAC-9291-2020
dc.authorwosidSantamaria, Jose/A-6415-2011
dc.contributor.authorAlbahri, A. S.
dc.contributor.authorHamid, Rula A.
dc.contributor.authorAbdulnabi, Ahmed Raheem
dc.contributor.authorAlbahri, O. S.
dc.contributor.authorAlamoodi, A. H.
dc.contributor.authorDeveci, Muhammet
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:38:53Z
dc.date.available2024-05-19T14:38:53Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis paper addresses various issues in the literature concerning adversarial attack detection in Vehicular Ad -hoc Networks (VANETs). These issues include the failure to consider both normal and adversarial attack perspectives simultaneously in Machine Learning (ML) model development, the lack of diversity preprocessing techniques for VANETs communication datasets, the inadequate selection guidelines for real-time adversarial attack detection models, and the limited emphasis on explainability in adversarial attack detection. In this study, we propose an original fuzzy decision -making framework that incorporates multiple fusion standpoints. Our framework aims to evaluate multi -ML models for real-time adversarial attack detection in VANETs, focusing on three stages. The first stage involves identifying and preprocessing Dedicated Short -Range Communication (DSRC) data using standard and fusion preprocessing approaches. Two communication scenarios, normal and jammed, are considered, resulting in two DSRC datasets. In the second stage, we develop multi -ML models based on the DSRC datasets using standard preprocessing and feature fusion preprocessing for dataset-1 and dataset-2, respectively. The third stage evaluates the multi -ML models using a fuzzy decision -making approach based on the Fuzzy Decision by Opinion Score Method (FDOSM) and an adversarial attack decision fusion matrix. The External Fusion Decision (EFD) settings of the FDOSM address individual ranking variance, provide a unique rank and select the best model. Experimental results demonstrate that the K -Nearest Neighbors Algorithm (kNN) model achieves the highest explain score of 0.2048 in dataset-1 using standard preprocessing, while the Random Forest (RF) model applied to dataset-2 using fusion preprocessing emerges as the most robust and golden model against adversarial attacks, with a score of 0.1819. This finding suggests that the fusion preprocessing approach using Principal Component Analysis (PCA) is more suitable for addressing normal and adversarial attack perspectives. Furthermore, our fuzzy framework undergoes evaluation in terms of systematic rank, sensitivity analysis, explainability analysis, and comparison analysis. Overall, this framework provides valuable insights for researchers and practitioners in VANETs, informing the execution, selection, and interpretation of multi -ML models to tackle adversarial attack detection problems effectively. The new fuzzy framework demonstrates that multiML models based on feature fusion preprocessing are more effective.en_US
dc.description.sponsorshipAustralian Government: Australian Research Council (ARC) Industrial Transformation Training Centre (ITTC) for Joint Biomechanics [IC190100020]; QUT ECR SCHEME 2022, The Queensland University of Technologyen_US
dc.description.sponsorshipThe authors would like to acknowledge the support received through the following funding schemes of the Australian Government: Australian Research Council (ARC) Industrial Transformation Training Centre (ITTC) for Joint Biomechanics under grant (IC190100020) . The authors also would like to acknowledge the support received through the QUT ECR SCHEME 2022, The Queensland University of Technology.en_US
dc.identifier.doi10.1016/j.inffus.2023.102208
dc.identifier.issn1566-2535
dc.identifier.issn1872-6305
dc.identifier.scopus2-s2.0-85182520876en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.inffus.2023.102208
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4641
dc.identifier.volume105en_US
dc.identifier.wosWOS:001165048800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofInformation Fusionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectAdversarial Attack Detectionen_US
dc.subjectFuzzy Decision -Makingen_US
dc.subjectMachine Learningen_US
dc.subjectVehicular Ad -Hoc Networksen_US
dc.subjectVaneten_US
dc.subjectExplainabilityen_US
dc.titleFuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in Vehicular Ad-hoc Networksen_US
dc.typeArticleen_US

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