Unsupervised Anomaly Detection for Container Cloud Via BILSTM-Based Variational Auto-Encoder

Abstract

The appearance of container technology has profoundly changed the development and deployment of multi-tier distributed applications. However, the imperfect system resource isolation features and the kernel-sharing mechanism will introduce significant security risks to the container-based cloud. In this paper, we propose a real-time unsupervised anomaly detection system for monitoring system calls in container cloud via BiLSTM-based variational auto-encoder (VAE). Our proposed BiLSTM-based VAE network leverages the generative characteristics of VAE to learn the robust representations of normal patterns by reconstruction probabilities while being sensitive to long-term dependencies. Our evaluations using real-world datasets show that the BiLSTM-based VAE network achieves excellent detection performance without introducing significant running performance overhead to the container platform.