Poster: Using machine learning to infer network structure from security metadata

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dc.check.date2026-07-10en
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisheren
dc.contributor.authorKhalid, Asfaen
dc.contributor.authorMurphy, Seán Ógen
dc.contributor.authorSreenan, Cormac J.en
dc.contributor.authorRoedig, Utzen
dc.contributor.funderHorizon 2020 Framework Programmeen
dc.contributor.funderEnterprise Irelanden
dc.date.accessioned2025-12-04T11:22:54Z
dc.date.available2025-12-04T11:22:54Z
dc.date.issued2025-07-10en
dc.description.abstractIn distributed cloud-edge environments, data-driven decision-making is essential for enhancing operational efficiency and maintaining a competitive advantage. Achieving this requires strong guarantees of data integrity and authenticity, as any compromise can lead to inaccurate insights, loss of trust, and financial damage. To address the cybersecurity risks posed by data transmission across complex, heterogeneous networks, Data Confidence Fabrics have been introduced. These enhance data security by generating metadata at each stage of transmission and storing it using distributed ledgers, which ensures the immutability and verifiability of this metadata. However, despite these benefits, the public accessibility of ledgers introduces significant privacy concerns. While previous research has focused on hostname obfuscation to protect network structure, timestamps often remain exposed, creating an exploitable vulnerability. We demonstrate that one can use K-means clustering on exposed timestamp patterns to reconstruct the obfuscated network structure, even when hostnames are fully obfuscated. Our findings reveal a critical gap in existing metadata protection mechanisms and highlight the need for defense against timestamp-based inference attacks.en
dc.description.sponsorshipHorizon 2020 Framework Programme (101097560); Enterprise Ireland (No: IR-2022-0065); Science Foundation Ireland (13/RC/2077 P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationKhalid, A., Murphy, S. Ó., Sreenan, C. J., Roedig, U. (2025) 'Poster: Using machine learning to infer network structure from security metadata', in Egele, M., Moonsamy, V., Gruss, D. and Carminati, M. (eds.) Detection of Intrusions and Malware, and Vulnerability Assessment. DIMVA 2025. Lecture Notes in Computer Science, vol 15748, pp. 93-99. Springer, Cham. https://doi.org/10.1007/978-3-031-97623-0_6en
dc.identifier.doi10.1007/978-3-031-97623-0_6en
dc.identifier.endpage99en
dc.identifier.isbn9783031976223en
dc.identifier.isbn9783031976230en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.journaltitleLecture Notes in Computer Scienceen
dc.identifier.startpage93en
dc.identifier.urihttps://hdl.handle.net/10468/18336
dc.identifier.volume15748en
dc.language.isoenen
dc.publisherSpringer Nature Switzerlanden
dc.relation.ispartofDetection of Intrusions and Malware, and Vulnerability Assessment. DIMVA 2025, Graz, Austria, 9-11 July 2025en
dc.rights© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG.en
dc.subjectData Confidence Fabricsen
dc.subjectK-meansen
dc.subjectMachine learningen
dc.subjectNetwork structureen
dc.subjectSecurity metadataen
dc.titlePoster: Using machine learning to infer network structure from security metadataen
dc.typeConference itemen
dc.typebook-chapteren
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