CDL: Classified Distributed Learning for Detecting Security Attacks in Containerized Applications

Abstract

Containers have been widely adopted in production computing environments for its efficiency and low overhead of isolation. However, recent studies have shown that containerized applications are prone to various security attacks. Moreover, containerized applications are often highly dynamic and short-lived, which further exacerbates the problem. In this paper, we present CDL, a classified distributed learning framework to achieve efficient security attack detection for containerized applications. CDL integrates online application classification and anomaly detection to overcome the challenge of lacking sufficient training data for dynamic short-lived containers while considering diversified normal behaviors in different applications. We have implemented a prototype of CDL and evaluated it over 33 real world vulnerability attacks in 24 commonly used server applications. Our experimental results show that CDL can reduce the false positive rate from over 12% to 0.24% compared to traditional anomaly detection schemes without aggregating training data. By introducing application classification into container behavior learning, CDL can improve the detection rate from catching 20 attacks to 31 attacks before those attacks succeed. CDL is light-weight, which can complete application classification and anomaly detection for each data sample within a few milliseconds.