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Using Containers And Kubernetes To Increase The Efficacy Of Anomaly Detection

The maturation of the container ecosystem coincides with the emergence of Kubernetes as a de-facto orchestrator to launch a container application. This new declarative design and eternal workload pave the way for the detection operating model and a completely new response. You can get more information about  kubernetes storage solution via https://kubevious.io/blog/post/comparing-top-storage-solutions-for-kubernetes/.

Kubernetes storage solution increases and improves traditional detection approaches such as anomalous detection. In Kubernetes, the detection of anomalies consists of monitoring the normal behavior of the application to study the general behavior of the application, set the basis for the activities obtained from the information obtained during the training phase, and then use the basis to measure future events, including reading and writing files, and the execution process.

 Anything that is significantly beyond normal limits is considered abnormal and must be investigated. Kubernetes and containers offer developers and operators unique ways to explicitly state the environment where their application has to run. 

In traditional VM infrastructure, it is difficult to effectively determine application activities. Or, users can use one application container to define a series of minimum permits and use Kubernetes to provide general abstractions about service interactions. 

These careful controls can increase the detection of anomalies by determining what behavior is dangerous or harmless and highlights activities that violate consumer guidelines. As a result, the application attack area is much smaller, so the less likely that criminals will build themselves into infrastructure.