Container Security: The Dark Side of Cloud Computing
The advent of more powerful processors in the early 2000’s started the computing revolution that led to what we now call the cloud. With single hardware instances able to run dozens, if not hundreds of virtual machines concurrently, businesses could offer their users multiple services and applications that would otherwise have been financially impractical, if not impossible.
The Limitations of Virtual Machines
But virtual machines (VMs) have several downsides. Often, an entire virtualised operating system is overkill for many applications, and although very much more malleable, scalable, and agile than a fleet of bare-metal servers, VMs still require significantly more memory and processing power, and are less agile than the next evolution of this type of technology – containers.
Container Security Risks
Container deployments and their tooling bring specific security challenges to those charged with running apps and services, whether manually piecing together applications with choice containers, or running in production with orchestration at scale. Some of the specific risks include:
- Misconfiguration: Complex applications are made up of multiple containers, and misconfiguration – often only a single line in a .yaml file – can grant unnecessary privileges and increase the attack surface.
- Vulnerable Container Images: In 2022, over 1,600 images identified as malicious in Docker Hub, in addition to many containers stored in the repo with hard-coded cloud credentials, ssh keys, and NPM tokens.
- Orchestration Layers: For larger projects, orchestration tools such as Kubernetes can increase the attack surface, usually due to misconfiguration and high levels of complexity.
Container Security with Machine Learning
The specific challenges of container security can be addressed using machine learning algorithms trained on observing the components of an application when it’s ‘running clean.’ By creating a baseline of normal behaviour, machine learning can identify anomalies that could indicate potential threats from unusual traffic, unauthorised changes to configuration, odd user access patterns, and unexpected system calls.
Conclusion
Machine learning can reduce the risk of data breach in containerised environments by working on several levels. Anomaly detection, asset scanning, and flagging potential misconfiguration are all possible, plus any degree of automated alerting or amelioration are relatively simple to enact.
Frequently Asked Questions
Q: What are the security risks associated with container deployments?
A: Misconfiguration, vulnerable container images, and orchestration layers are all potential security risks.
Q: How can machine learning help address these risks?
A: Machine learning can detect anomalies, scan for vulnerabilities, and flag potential misconfigurations, making it easier to prevent data breaches.
Q: Is it possible to reduce the risk of data breach in containerised environments?
A: Yes, machine learning can reduce the risk of data breach by detecting anomalies, scanning for vulnerabilities, and flagging potential misconfigurations.
