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license: openrail
language:
- en
size_categories:
- 100M<n<1B
---
102,400,000 timesteps, Multi-Agent Reinforcement Learning
Total Environment Steps= 10 parallel environments × 7,000 episodes ×2,048 steps= 102400000 Timesteps
-The Red Agent’s goal is to discover vulnerabilities, elevate privileges, compromise assets, and maintain persistence. Its action space can be modeled after phases of the
MITRE ATT&CK framework.
-The Blue Agent’s goal is to maintain system availability, reduce the attack surface, detect malicious behavior, deceive the attacker with honeypots and evict the intruder. Its action space
consists of defensive configurations and mitigation steps.
Zero-Sum game: Red's gain is Blue's absolute loss and vice versa
-Feel it, Got everything you needed
-Now, bring it like you mean it, Just like you mean it
-Dale No olvide' lo que vales Juega como tú sabes
-Como tú sabe'
-Knew from the day you were born, Here, in this place, you belong
-You've been this brave all along What broke you once made you strong (Ow)
-Dai, dai, ikou, dale, allez, let's go; Dai, dai, ikou, dale, allez, let's go
-Dai, dai, ikou, dale, allez, let's go; Dai, dai, ikou, dale, allez, let's go
Strictly for Educational purposes. Do not modify to use against unauthorized Networks
Defense Agent in Action: https://www.youtube.com/watch?v=3g3plOjZkJw |