Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests

Authors: Hanlin Zhou, Huah Yong Chan, Jingfei Ni, Mengchun Wu, Qing Deng
Published: 2026-01-20

Source: https://arxiv.org/abs/2601.13515

PDF: https://arxiv.org/pdf/2601.13515

AIにより推定されたラベル

性能評価 機械学習応用 攻撃トラフィック管理

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

Abstract

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.