SPARK: Secure Predictive Autoscaling for Robust Kubernetes

Computer Science > Cryptography and Security [Submitted on 27 Mar 2026] SPARK: Secure Predictive Autoscaling for Robust Kubernetes Zhijun Jiang, Amin Milani Fard Achieving high availability and robust security in Kubernetes requires more than reactive scaling and standard perimeter firewalls. Traditional autoscalers, such as HPA, often fail to react quickly to traffic spikes and cannot distinguish between legitimate flash crowds and DDoS attacks. We present an open-source toolchain to provide a traffic-aware autoscaling approach that utilizes an eBPF-based networking layer to enforce security policies at the kernel level while orchestrating scaling decisions based on predictive models. Our results demonstrate that the predictive approach reduces timeout errors by 32% during sudden traffic surges compared to standard reactive scaling, while ensuring immediate network convergence and layer 7 security isolation for newly scaled pods. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.26833 [cs.CR]   (or arXiv:2603.26833v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.26833 Focus to learn more Submission history From: Amin Milani Fard [view email] [v1] Fri, 27 Mar 2026 05:23:10 UTC (60 KB) Access Paper: HTML (experimental) 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?)