# wtsxDev/Machine-Learning-for-Cyber-Security

Curated list of tools and resources related to the use of machine learning for cyber security

Repository: https://github.com/wtsxDev/Machine-Learning-for-Cyber-Security
Canonical: https://ross.abutalabs.com/products/machine-learning-for-cyber-security
License Family: other
Last push: 2020-10-01T04:05:19+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3517, "days_push": 2162, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1534, forks 438 (observed 2026-08-28T04:04:59.721230+00:00)

## What it is
A curated awesome-list of tools, datasets, papers, books, tutorials, and courses on applying machine learning to cyber security. It is a reference resource rather than runnable software.

## Use cases
- find datasets for training intrusion detection models
- research papers on malware detection with machine learning
- learn machine learning for cyber security
- find security ML tutorials and courses
- discover tools for ML-based security analytics
- study password guessability and adversarial ML research

## When to choose
- you need a starting point for security-focused ML research or learning
- you want curated links to security datasets and papers

## When to avoid
- you need a working ML security tool or library to run
- you need actively maintained software with a license

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, security, developer-tools
- domain: security, machine-learning, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, curated-resources, cybersecurity, datasets, research-papers, intrusion-detection, malware-detection

## Member repositories
- wtsxDev/Machine-Learning-for-Cyber-Security (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:59.721230+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:31:13.705967+00:00, confidence not recorded.
  - readme: https://github.com/wtsxDev/Machine-Learning-for-Cyber-Security (fetched 2026-08-28T04:04:59.721230+00:00, sha 43bb5b770ecb)
- Data as of 2026-08-30T08:39:29.467469+00:00.