CSLE: A Reinforcement Learning Platform for Autonomous Security Management
Summary: arXiv:2604.15590v1
Type: cross
Abstract: 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.
Introduction
With the growing complexity of networked systems, the need for effective security management has never been more critical. Traditional methods often fall short in adapting to dynamic threats. Reinforcement learning (RL) presents a solution, but its application has largely remained within controlled simulation environments. This limitation raises concerns about the practical applicability of existing models in real-world scenarios.
Overview of CSLE
The CSLE platform is designed to overcome the limitations of conventional reinforcement learning methods by providing a dual-system approach. Conceptually, CSLE encompasses two systems:
- Emulation System: This system replicates key components of the target system within a virtualized environment. It facilitates the gathering of measurements and logs, which are essential for identifying a system model, such as a Markov decision process.
- Simulation System: Once the system model is established, the simulation system enables efficient learning of security strategies through simulations. These strategies are then evaluated and refined in the emulation system, thereby bridging the gap between theoretical frameworks and operational performance.
Use Cases of CSLE
To illustrate the capabilities of CSLE, we demonstrate its effectiveness through four distinct use cases:
- Flow Control: Managing data flow within the network to prevent congestion and potential breaches.
- Replication Control: Ensuring that critical data is replicated securely to avoid data loss and unauthorized access.
- Segmentation Control: Dividing the network into segments to contain threats and limit potential damage.
- Recovery Control: Implementing strategies to recover from security breaches and restore normal operations swiftly.
Conclusion
Through the implementation of these use cases, CSLE demonstrates its potential to achieve near-optimal security management within environments that closely approximate operational systems. By providing a platform that enables experimentation and learning in realistic conditions, CSLE paves the way for the next generation of autonomous security solutions. As the field of reinforcement learning continues to evolve, platforms like CSLE will be essential in bridging the gap between theoretical advancements and practical applications in network security management.