Container Runtime Monitoring

Container Runtime Monitoring: Scalable, Intelligent Forensics at Runtime

Container runtime monitoring is becoming increasingly vital as organizations adopt microservices and deploy applications across massive, distributed environments. Traditional security and monitoring solutions often fall short in containerized ecosystems, where the scale, speed, and volatility of workloads demand more intelligent and efficient approaches. In this post, we dive into a groundbreaking method for container runtime monitoring that leverages advanced deep learning to identify anomalies and optimize forensic data collection at scale.

The challenge is straightforward but far from simple: How can we monitor thousands of containers in real time, detect when they become unstable or compromised, and collect the precise forensic data needed for incident response or fault analysis—without overwhelming our infrastructure?

One effective technique in modern container runtime monitoring involves eBPF-based tools like Sysdig and Chisel. These extend the Linux kernel to intercept system calls, allowing security teams to capture syscall-level forensics while applications are running. However, this approach comes with serious cost implications. Capturing and storing every detail in high-volume container environments drives up CPU usage, network bandwidth, and storage needs—quickly becoming unsustainable in production.

To tackle these scalability concerns, the Econox and SRI teams are developing a more intelligent solution built on deep learning, specifically a Variational Autoencoder (VAE). This neural network-based system learns the “normal” system call behavior of applications. Once trained, it can detect deviations in real time, triggering targeted forensic data capture only when anomalies are detected. This smart publishing mechanism dramatically reduces resource usage while still providing actionable insights when it matters most.

The VAE operates by encoding sequences of system call statistics—collected via eBPF during controlled, normal runs—into compact vectors that represent typical behavior. At runtime, the system compares live data against the trained model. When reconstruction errors spike, it flags a potential anomaly, initiating full forensic logging for that time interval. Otherwise, it summarizes and publishes a compact representation of normal behavior.

In experiments using an application called Focal, researchers compared the performance of standard forensic publishing against VAE-optimized publishing across 10 to 50 container instances. The results were striking. The VAE-based system consistently outperformed the standard method in terms of CPU consumption, data volume, and processing time. For example, with 50 containers, the VAE system processed forensic data in just 510 milliseconds per interval—versus over 30 seconds for the traditional method, which was nearing saturation.

In addition to reducing CPU load, the VAE system slashed network traffic by three orders of magnitude and cut storage needs by four. Standard publishers logged full system call streams regardless of behavior, while the VAE summarized stable periods and only captured full detail when something unusual occurred—like the launch of an unexpected shell or a potential cryptominer attack.

Perhaps most importantly, the VAE provides real-time anomaly detection as an integrated part of the monitoring process. It doesn’t just collect data for future analysis; it actively flags issues as they happen, streamlining incident response and helping teams quickly identify which containers are behaving abnormally.

This approach to container runtime monitoring is a game-changer for organizations struggling to balance observability with performance. It enables deep process-level inspection across massive container fleets—without crippling your infrastructure. The smart combination of eBPF and machine learning offers a practical path forward in securing modern cloud-native environments.

If you’re looking to scale runtime visibility, detect threats as they happen, and control resource costs, VAE-driven container runtime monitoring might be your best next move.