Velora
Velora, a Liquid RL framework for NeuroFlow agents, empowering Autonomous Cyber Defence.
Velora is a lightweight and modular framework built on top of powerful libraries like Gymnasium [] and PyTorch []. It is home to a new type of RL agent called NeuroFlow (NF) that specializes in Autonomous Cyber Defence through a novel Deep Reinforcement Learning (RL) approach we call Liquid RL.
Benefits¶
- Explainability: NF agents use Liquid Neural Networks [] (LNNs) and Neural Circuit Policies [] (NCPs) to model Cyber system dynamics, not just data patterns. Also, they use sparse NCP connections to mimic biological efficiency, enabling clear, interpretable strategies via a labeled Strategy Library.
- Adaptability: NF agents dynamically grow their networks using a fitness score, adding more neurons to a backbone only when new Cyber strategies emerge, keeping agents compact and robust.
- Planning: NF agents use a Strategy Library and learned environment model to plan strategic sequences for proactive Cyber defense.
- Always Learning: using EWC [], NF agents refine existing strategies and learn new ones post-training, adapting to evolving Cyber threats like new attack patterns.
- Customizable: NF agents are PyTorch-based [], designed to be intuitive, easy to use, and modular so you can easily build your own!
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Getting Started
What are you waiting for?!
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Open Source, MIT
Velora is licensed under the MIT License.
Active Development¶
Velora is a tool that is continuously being developed. There's still a lot to do to make it a great framework, such as detailed API documentation, and expanding our NeuroFlow agents.
Our goal is to provide a quality open-source product that works 'out-of-the-box' that everyone can experiment with, and then gradually fix unexpected bugs and introduce more features on the road to a v1 release.
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Roadmap
Check out what we have planned for Velora.