Graph Neural Network-Based DDoS Protection for Data Center Infrastructure

Computer Science > Cryptography and Security [Submitted on 14 Mar 2026] Graph Neural Network-Based DDoS Protection for Data Center Infrastructure Kartikeya Sharma, Craig Jacobik In light of rising cybersecurity threats, data center providers face growing pressure to protect their own management infrastructure from Distributed Denial-of-Service (DDoS) attacks. While tenant-managed cages generally fall outside the data center's direct security purview, a successful DDoS assault on core provider systems can indirectly disrupt network services. To address this availability assault, the authors developed a Graph Neural Network (GNN) based detection system which leverages Graph U-Nets to automatically classify and mitigate DDoS traffic. Although the model was developed using open-source network flows rather than proprietary data center logs, the model effectively identifies multi-layer DDoS attacks that resemble the malicious patterns threatening modern data centers. Adopting this system to data center environments requires minimal changes to existing operational workflows and processes. Specifically, the GNN based system can be integrated at critical areas within a data center's network infrastructure. Our model achieved an F1 score of over 95% when evaluated on various open-source datasets, significantly reducing the likelihood of service disruptions and reputational damage. This Graph U-Nets architecture delivers unprecedented precision (98.5%) in complex cloud environments, thereby helping data center operators uphold reliable service availability and increase customer trust and goodwill in an era of increasingly sophisticated cyber threats. Comments: 14 pages, 3 figures, 3 tables, PNSQC Proceedings Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.13694 [cs.CR]   (or arXiv:2603.13694v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.13694 Focus to learn more Journal reference: PNSQC 2025 Proceedings, Pacific Northwest Software Quality Conference, 2025, pp 293-307 Submission history From: Craig Jacobik [view email] [v1] Sat, 14 Mar 2026 01:57:12 UTC (516 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 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?)