Scalable graph-aware edge representation learning for wireless IoT intrusion detection

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorJiang, Zhenyu
dc.contributor.authorLi, Jiliang
dc.contributor.authorHu, Qinnan
dc.contributor.authorMeng, Weizhi
dc.contributor.authorPedrycz, Witold
dc.contributor.authorSu, Zhou
dc.date.accessioned2025-04-18T10:24:21Z
dc.date.available2025-04-18T10:24:21Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractNetwork intrusion detection systems (NIDSs) have emerged as a frontline defense against the potential attacks in the wireless Internet of Things (IoT) networks. However, existing machine learning methods follow an unstructured data processing patterns and can barely incorporate all information due to the network dynamicity as well as the data imbalance. In this study, we propose the graph isomorphism network model based on the edge (GINE), an innovative graph-based algorithm tailored to pinpoint the malicious network traffic within the wireless IoT networks. Specifically, we initiate by presenting the wireless IoT network graph, capturing the global topological interactions of its edges. Subsequently, we design an edge representation learning algorithm, capable of encoding network data frames in a discerning pattern-aware manner. Moreover, we integrate a data interpolation module into the edges of our structured graph data targeting at the data imbalance, which fosters a more balanced distribution across the various classes of edges. Our empirical analysis on the selected wireless IoT intrusion data sets shows GINE's superiority, consistently outperforming the state-of-the-art methods in classification metrics, including accuracy, F1-score, false alarm rate, etc. Through a simulated wireless environment, we demonstrate GINE's robust scalability, even in unpredictable wireless networks.
dc.identifier.citationJiang, Z., Li, J., Hu, Q., Meng, W., Pedrycz, W., & Su, Z. (2024). Scalable Graph-Aware Edge Representation Learning for Wireless IoT Intrusion Detection. IEEE Internet of Things Journal.
dc.identifier.doi10.1109/JIOT.2024.3397364
dc.identifier.endpage26969
dc.identifier.issn23274662
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85193035341
dc.identifier.scopusqualityQ1
dc.identifier.startpage26955
dc.identifier.urihttp://dx.doi.org/10.1109/JIOT.2024.3397364
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7052
dc.identifier.volume11
dc.identifier.wosWOS:001291138800009
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of electrical and electronics engineers inc.
dc.relation.ispartofIEEE internet of things journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEdge Representation Learning
dc.subjectGraph Neural Network (GNN)
dc.subjectIntrusion Detection
dc.subjectWireless Network
dc.titleScalable graph-aware edge representation learning for wireless IoT intrusion detection
dc.typeArticle

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