Rule-based security monitoring of containerized workloads

In order to further support the secure operation of containerized environments and to extend already established security measures, we propose a rule-based security monitoring, which can be used for the detection of a variety of misuse and attacks. The capabilities of the open-source tools used to monitor containers are closely examined and the possibility of detecting undesired behavior is evaluated on the basis of various scenarios. Further, the limits of the approach taken and the associated performance overhead will be discussed. The results show that the proposed approach is effective in many scenarios and comes at a low performance overhead cost

Given 2n eyeballs, all quality flaws are shallow

We demonstrate the capabilities of the Microservice Artefact Observatory (MAO), a federated software quality assessment middleware. MAO’s extensible assessment tools continuously scan for quality flaws, defects and inconsistencies in microservice artefacts and observe runtime behaviour. The federation reduces bias and also increases the resilience and overcomes per-site failures, leading to a single, merged timeline of software quality. Already serving concurrently by n = 3 observant operators in Argentina and Switzerland, the federation is designed to become a community-wide consensus voting-based ground truth repository with query interfaces for large-scale software quality and evolution insights. These insights can be exploited for excluding buggy software before or after deployment, for optimised resource allocation, and further software management tasks

Rule-based Security Monitoring of Containerized Workloads

In order to further support the secure operation of containerized environments and to extend already established security measures, we propose a rule-based security monitoring, which can be used for the detection of a variety of misuse and attacks. The capabilities of the open-source tools used to monitor containers are closely examined and the possibility of detecting undesired behavior is evaluated on the basis of various scenarios. Further, the limits of the approach taken and the associated performance overhead will be discussed. The results show that the proposed approach is effective in many scenarios and comes at a low performance overhead cost