# microsoft/CyberBattleSim

An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.

Repository: https://github.com/microsoft/CyberBattleSim
Canonical: https://ross.abutalabs.com/products/cyberbattlesim
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-08-25T16:14:21+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 2115, "days_push": 8, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1784, forks 285 (observed 2026-08-28T04:05:35.946644+00:00)

## What it is
CyberBattleSim is a Python-based experimentation and research platform from Microsoft that simulates abstract enterprise network environments where automated attacker and defender agents interact. It provides an Open AI Gym interface so reinforcement learning algorithms can train agents to exploit vulnerabilities, move laterally across networks, and defend against such attacks.

## Use cases
- train reinforcement learning agents for cyber attack simulation
- research lateral movement attacks in simulated networks
- benchmark RL algorithms on cybersecurity environments
- simulate attacker versus defender interactions in enterprise networks
- study how network topology affects cyber attack propagation
- build a custom gym environment for security research

## When to choose
- you need a safe, abstract sandbox for cybersecurity RL research
- you want to compare reinforcement learning agents on attack/defense tasks
- you are studying the effect of network topology and vulnerabilities on lateral movement

## When to avoid
- you need realistic network traffic or direct application to real-world systems
- you want production-grade penetration testing tools
- you need a polished game or commercial training product

## Facets
- artifact type: library
- maturity: active
- function: simulation, reinforcement-learning, agent-framework, security, penetration-testing
- domain: security, artificial-intelligence, reinforcement-learning, simulation
- platform: python, cross-platform
- tags: cybersecurity-simulation, openai-gym, attack-defense, lateral-movement, network-simulation, research-platform, research

## Member repositories
- microsoft/CyberBattleSim (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:35.946644+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 2026-08-30T03:24:16.099301+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/CyberBattleSim (fetched 2026-08-28T04:05:35.946644+00:00, sha a5bb1eb5b45a)
- Data as of 2026-08-30T08:39:29.467469+00:00.