Artifacts - CSLE: A Reinforcement Learning Platform for Autonomous Security Management - MLSys 2026

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Authors: Kim Hammar
Cited by: 0

Abstract

All results presented in this paper are fully reproducible using open-source software and data. To enable independent verification of our results and encourage future research, we release a complete set of artifacts that allow the community to build upon our work without additional engineering effort. Specifically, we provide the following artifacts:1. The source code of CSLE, our reinforcement learning platform for autonomous security management. Available at: https://github.com/Kim-Hammar/csle.2. Pre-built Docker images for creating digital twins. Available at: https://hub.docker.com/u/kimham.3. A video that demonstrates how to install CSLE. Available at: https://www.youtube.com/watch?v=l_g3sRJwwhc.4. A video that demonstrates the web interface of CSLE. Available at: https://www.youtube.com/watch?v=iE2KPmtIs2A.5. A dataset of attack statistics generated with CSLE. Available at: https://github.com/Kim-Hammar/csle/releases/download/v0.4.0/statistics_dataset_14_nov_22_json.zip.6. Platform documentation. Available at: https://github.com/Kim-Hammar/csle/blob/master/releases (PDF version) and https://kim-hammar.github.io/csle/ (web version).

Fields of Study & Subjects

Network Security and Intrusion Detection Information and Cyber Security Adversarial Robustness in Machine Learning

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
18 May 2026
Language
EN