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Tool Demo: Topology analysis with GPML for detection of cyberattacks in Water Distribution Networks

arXiv Security Archived Aug 08, 2026 ✓ Full text saved

arXiv:2608.05902v1 Announce Type: new Abstract: Water distribution networks depends on industrial control systems to integrate the physical process with communication network, making them vulnerable to cyberattacks that alter the traffic pattern and network behavior. Traditional detection approaches that rely on raw traffic or protocol information often oversee structural changes that are induced by such attacks. In this work, we presents a topology-driven approach for detection of cyberattacks

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    Computer Science > Cryptography and Security [Submitted on 6 Aug 2026] Tool Demo: Topology analysis with GPML for detection of cyberattacks in Water Distribution Networks Majed Jaber (ICube-Réseaux), Abdul Qadir Khan, Ankush Meshram, Julien Michel (LRE, ICube), Côme Frappé - - Vialatoux (ICube, LRE), Pierre Parrend (ICube, LRE) Water distribution networks depends on industrial control systems to integrate the physical process with communication network, making them vulnerable to cyberattacks that alter the traffic pattern and network behavior. Traditional detection approaches that rely on raw traffic or protocol information often oversee structural changes that are induced by such attacks. In this work, we presents a topology-driven approach for detection of cyberattacks in water distribution networks based on Graph Processing for Machine Learning (GPML) framework. The raw traffic is transformed into dynamic graphs, from which community and spectral metrics are extracted and analyzed for any structural and communication modifications with time. The proposed methodology is evaluated on three industrial water distribution datasets such as HITL, SWaT, and CrossTest. Spectral and community graph metrics improve the model performance in detection of cyber and pyhiscal attacks across the three datasets. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.05902 [cs.CR]   (or arXiv:2608.05902v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.05902 Focus to learn more Journal reference: IEEE/IFIP Network Operations and Management Symposium 2026 / MCT Management of Complex Threats, May 2026, Rome, France Submission history From: Pierre Parrend [view email] [via CCSD proxy] [v1] Thu, 6 Aug 2026 11:31:38 UTC (694 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Aug 08, 2026
    Archived
    Aug 08, 2026
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