CSLE: A Reinforcement Learning Platform for Autonomous Security Management
Reinforcement learning is a promising approach to autonomous and adaptive security management in networked systems. However, current reinforcement learning solutions for security management are mostly limited to simulation environments and it is unclear how they generalize to operational systems. In this paper, we address this limitation by presenting CSLE: a reinforcement learning platform for autonomous security management that enables experimentation under realistic conditions. Conceptually, CSLE encompasses two systems. First, it includes an emulation system that replicates key components of the target system in a virtualized environment. We use this system to gather measurements and ...
| Authors | Kim Hammar |
|---|---|
| Primary category | cs.CR |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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