Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918296654184448 |
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| author | Zhou, Hanlin Chan, Huah Yong Ni, Jingfei Wu, Mengchun Deng, Qing |
| author_facet | Zhou, Hanlin Chan, Huah Yong Ni, Jingfei Wu, Mengchun Deng, Qing |
| contents | In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13515 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests Zhou, Hanlin Chan, Huah Yong Ni, Jingfei Wu, Mengchun Deng, Qing Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments. |
| title | Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests |
| topic | Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2601.13515 |