Automated Detection of Misconfiguration in Kubernetes YAML Files Using Machine Learning

Authors

Hariprasad Sivaraman

Abstract

Kubernetes is the game-changer as far as managing and orchestrating cloud-native infrastructure; however, this complex “YAML”-based configuration structure can become a problem because it introduces human (and/or automation) error which in turn disrupts operations. These YAML files, when misconfigured and malformed lead to skirmishes such as not allocating resources appropriately, facing security problems due to its exposure or additional downtime of the application. This paper presents Vela, a new ML-based solution that integrates anomaly detection and sequence modeling to automatically identify misconfigurations in Kubernetes YAML files. The approach in this paper uses Isolation Forests to detect outliers and Long Short-TermMemory (LSTM)-based sequence models to detect hierarchical and structural misconfigurations, together allowing our model to detect both common and novel errors very well. It fits nicely into CI/CD pipelines and provides real-time feedback, ensuring minimal deployment failures. With extensive experiments in production-like Kubernetes environments, the proposed method can be a promising solution to mitigate operational risks caused by YAML misconfigurations and achieve significant improvement in configuration management.

Keywords

Kubernetes, Misconfiguration Detection, YAML Files, Machine Learning, Natural Language Processing, Anomaly Detection, LSTM Networks

Automated Detection of Misconfiguration in Kubernetes YAML Files Using Machine Learning. Hariprasad Sivaraman. 2023. IJIRCT, Volume 9, Issue 1. Pages 1-6. https://www.ijirct.org/viewPaper.php?paperId=2411107