awesome-vulnerability-assessment
An ever-growing list of resources for data-driven vulnerability assessment and prioritization (by lhmtriet)
awesome-seml
A curated list of articles that cover the software engineering best practices for building machine learning applications. (by SE-ML)
| awesome-vulnerability-assessment | awesome-seml | |
|---|---|---|
| 1 | 1 | |
| 78 | 1,195 | |
| - | 0.9% | |
| 2.3 | 0.0 | |
| about 1 year ago | about 2 months ago | |
| MIT License | Creative Commons Zero v1.0 Universal |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-vulnerability-assessment
Posts with mentions or reviews of awesome-vulnerability-assessment.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Seeking Advice on Developing a Vulnerability Management Program
At first glance the tool selection looks a bit counterintuitive - will your focus be EASM, vulnerability assessment (you are not managing anything unless you include risk acceptance/mitigation and remediation) or automated (atomic) red teaming? For easy exploitability checks have a look at Prelude Operator; Nuclei as a modern scanner, OpenVAS to represent the traditional approach. For theory backing check here: https://github.com/lhmtriet/awesome-vulnerability-assessment
awesome-seml
Posts with mentions or reviews of awesome-seml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-16.
-
[D] How to maintain ML models?
They also have an awesome-seml repo on GitHub outlining many (scientific) articles as well as tools and frameworks that may help you out in implementing these best practices.
What are some alternatives?
When comparing awesome-vulnerability-assessment and awesome-seml you can also consider the following projects:
subaru-starlink-research - Subaru StarLink persistent root code execution.
MLOps - MLOps examples
yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.
MLflow - Open source platform for the machine learning lifecycle
dvc - 🦉 ML Experiments and Data Management with Git
mllint - `mllint` is a command-line utility to evaluate the technical quality of Python Machine Learning (ML) projects by means of static analysis of the project's repository.