(untitled) (Comprehensive Journal of Science, 2026)
With the rapid evolution of container technologies in modern cloud computing architectures, container breakouts constitute a serious threat, as they exploit security vulnerabilities in shared isolation at the operating system kernel level. Traditional detection systems often lack kernel-level visibility, requiring operating system kernel monitoring for real-time analysis. This paper presents a hybrid protection framework that combines operating system kernel monitoring (eBPF) with accelerated cloud processing via Google Colab. The proposed system employs a multilayer detection mechanism, a lightweight behavioral rule layer for initial filtering, followed by a two-stage machine learning process. This latter stage includes an autoencoder model for anomaly detection and a random forest model for final classification. The experimental results achieved a classification accuracy of 96.8% and an F1-score of 96.3%, indicating a promising balance between precision and recall. The system also achieved an overall response time of 19.32 ms, with CPU resource consumption of less than 4.2%. These results indicate the effectiveness of the architectural separation between a low-level monitoring layer and a cloud-based analytics layer, providing a practical model for proactive defense systems in high-density container environments.