The Cyber Security Learning Environment (CSLE) is an innovative platform crafted specifically for evaluating and developing reinforcement learning agents targeting control problems in the dynamic field of cybersecurity. Think of it as a high-tech playground where AI and cyber protection converge to train our digital defenders.
Main Features
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Emulation System:
CSLE includes a solution for emulating extensive IT infrastructures, cyber-attacks, and client populations. This system, based on Linux containers, facilitates the collection of traces and the evaluation of security policies.
Note: The emulation system is primarily designed for distributed systems, such as compute clusters. It can operate on a laptop, but only small-scale emulations are feasible.
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Simulation System:
Here, we execute reinforcement learning algorithms and simulate Markov decision processes and games. Built in Python, it can seamlessly integrate with established machine learning libraries.
Note: The simulations are compatible with OpenAI Gym, allowing you to integrate your solutions with existing reinforcement learning implementations.
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Management System:
This system enables the efficient management of both emulation and simulation processes, accessible via Command-Line Interface (CLI), REST API, Python libraries, or a web interface. The management capabilities include:
- Starting and stopping emulations/simulations.
- Real-time monitoring of processes.
- Shell access to emulation components.
Documentation
Comprehensive documentation, including installation instructions and usage examples, can be found here. Additionally, a PDF version of the documentation is available here, and a detailed video walkthrough of the installation process can be accessed here.
Supported Releases
The following releases are currently supported:
- [v.0.7.0](https://github.com/Limmencsle/releases/tag/v0.7.0) – Last date of support: 2025-03-01
- [v.0.6.0](https://github.com/Limmencsle/releases/tag/v0.6.0) – Last date of support: 2024-12-24
- [v.0.5.0](https://github.com/Limmencsle/releases/tag/v0.5.0) – Last date of support: ~~2024-06-02~~
- [v.0.4.0](https://github.com/Limmencsle/releases/tag/v0.4.0) – Last date of support: ~~2024-02-07~~
- [v.0.3.0](https://github.com/Limmencsle/releases/tag/v0.3.0) – Last date of support: ~~2024-01-17~~
- [v.0.2.0](https://github.com/Limmencsle/releases/tag/v0.2.0) – Last date of support: ~~2023-10-30~~
- [v.0.1.0](https://github.com/Limmencsle/releases/tag/v0.1.0) – Last date of support: ~~2023-06-06~~
Build Status Workflow
Check the current build status for various components of CSLE:
Troubleshooting
If you encounter any issues while working with CSLE, consider the following troubleshooting tips:
- Ensure your Linux containers are correctly set up if facing emulation problems.
- Check if your simulations integrate properly with OpenAI Gym for the simulation system.
- Review the permissions and configurations of the management system for accessibility.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.