Case study
ML Vulnerability Prioritisation
Classical ML pipeline for CVE risk classification.
Classical ML baselines for CVE risk classification and prioritisation.
At a glance
ML Vulnerability Prioritisation
Classical ML pipeline for CVE risk classification.
- Why it matters
- Metrics and dataset notes.
- My role
- Project owner
- Stack / tools
- Python, scikit-learn, TF-IDF, pandas, CSV datasets
- Status
- Working
- Estimated read
- 6-8 minutes
Role
Project owner
Stack
Python, scikit-learn, TF-IDF, pandas
Status
Working
Evidence
Metrics and dataset notes.
What problem this project tries to solve and where the evidence starts.
Context
Project context
Notes, evidence, and next steps for this project.
Metrics and dataset notes.
How the main pieces fit together.
Architecture
System notes
Architecture notes and diagrams belong here.
Add model comparison metrics and validation notes.
todonotedataset pipeline placeholderAdd feature extraction and CVE data notes.
todoWhat the project was built with.
Implementation
Metrics
What metrics exist and what is still missing.
Model comparison
pending
Add validated model comparison output.
What exists now and what is still pending.
Evidence
Evidence tracked in Git
Missing assets stay clearly marked.
model metrics placeholder
Add model comparison metrics and validation notes.
dataset pipeline placeholder
Add feature extraction and CVE data notes.
What I would improve next.
Roadmap
Why this work matters for engineering roles.
Employment relevance
The work touches controllers, APIs, datasets, logs, and tests.
Next action
That is the main path.
You can check the CV, GitHub, or the local Black Ice view next.
Connect
Let's connect
I am looking for graduate and junior opportunities in cybersecurity, machine learning, secure software engineering, and network/security research.