| --- |
| license: openrail |
| language: |
| - en |
| size_categories: |
| - 100M<n<1B |
| --- |
| |
| 102,400,000 timesteps, Multi-Agent Reinforcement Learning |
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| Total Environment Steps= 10 parallel environments × 7,000 episodes ×2,048 steps= 102400000 Timesteps |
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| -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. |
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| -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. |
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| Zero-Sum game: Red's gain is Blue's absolute loss and vice versa |
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| -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' |
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| -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 |
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| Strictly for Educational purposes. Do not modify to use against unauthorized Networks |
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| Defense Agent in Action: https://www.youtube.com/watch?v=3g3plOjZkJw |
