Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests
This addresses security and resource management issues for Kubernetes users, but it is incremental as it applies an existing machine learning method to a specific domain.
The paper tackles the problem of managing attack traffic in Kubernetes by using Random Forests to predict attacks and dynamically adjust HPA parameters, resulting in a lower incidence of 5XX status codes and effective isolation of attack traffic.
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.