Security Audit of Docker Container Images in Cloud Architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Containers technology radically changed the ways for packaging applications and deploying them as services in cloud environments. According to the recent report on security predictions of 2020 by Trend Micro, the vulnerabilities in container components deployed with cloud architecture have been one of the top security concerns for development and operations teams in enterprises. Docker is one of the leading container technologies that automate the deployment of applications into containers. Docker Hub is a public repository by Docker for storing and sharing the Docker images. These Docker images are pulled from the Docker Hub repository and the security of images being used from the repositories in any cloud environment could be at risk. Vulnerabilities in Docker images could have a detrimental effect on enterprise applications. In this paper, the focus is on securing the Docker images using vulnerability centric approach (VCA) to detect the vulnerabilities. A set of use cases compliant with the NIST SP 800-190 Application Container Security Guide is developed for audit compliance of Docker container images with the OWASP Container Security Verification Standards (CSVS). In this paper, firs vulnerabilities of Docker container images are identified and assessed using the VCA. Then, a set of use cases to identify presence of the vulnerabilities is developed to facilitate the security audit of the container images. Finally, it is illustrated how the proposed use cases can be mapped with the requirements of the OWASP Container Security Verification Standards. The use cases can serve as a security auditing tool during the development, deployment, and maintenance of cloud microservices applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it